Goal: Producing reproducible descriptive plots, bivariate descriptive statistics tables, multivariable regression

install.packages("compareGroups")
install.packages("DiagrammeR")
install.packages("DiagrammeRsvg")
install.packages("rsvg")
install.packages("ggpubr")
install.packages("hrbrthemes")
library(here)
## here() starts at /Volumes/GoogleDrive/My Drive/Fragile_Families/Air_Poll_PEGS/Prenatal-Particulate-Matter-Exposure-and-Saliva-DNA-Methylation-at-Ages-9-and-15-in-the-FFCW
library(compareGroups)
library(DiagrammeR)
library(DiagrammeRsvg)
library(rsvg)
## Linking to librsvg 2.48.4
library(ggplot2)
library(ggpubr)
library(hrbrthemes)
library(nlme)
hrbrthemes::import_roboto_condensed() 
## You will likely need to install these fonts on your system as well.
## 
## You can find them in [/Library/Frameworks/R.framework/Versions/4.1/Resources/library/hrbrthemes/fonts/roboto-condensed]
date <- format(Sys.Date(), "%Y%m%d") 

load(file=here("Data", paste0("FFCW_AirPoll_DNAm_", date, ".rda")))
load(file=here("Data", paste0("FFCW_AirPoll_DNAm_Included_", date, ".rda")))
summary(include$birth.pm10)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   7.469  13.446  15.601  14.984  16.670  20.171     117
summary(include$peg.pm10.centstd)
##        V1          
##  Min.   :-0.98740  
##  1st Qu.:-0.45109  
##  Median :-0.19758  
##  Mean   :-0.08303  
##  3rd Qu.: 0.15643  
##  Max.   : 2.25690
cor(include$birth.pm10, include$peg.pm10.centstd, use="pairwise.complete.obs")
##            [,1]
## [1,] 0.02506577
cor.test(include$birth.pm10, include$peg.pm10.centstd, use="pairwise.complete.obs")
## 
##  Pearson's product-moment correlation
## 
## data:  include$birth.pm10 and include$peg.pm10.centstd
## t = 0.94584, df = 1423, p-value = 0.3444
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.02689794  0.07689438
## sample estimates:
##        cor 
## 0.02506577
cor.test(include$birth.pm10[include$childteen=="C"], include$peg.pm10.cent[include$childteen=="C"])
## 
##  Pearson's product-moment correlation
## 
## data:  include$birth.pm10[include$childteen == "C"] and include$peg.pm10.cent[include$childteen == "C"]
## t = 1.2021, df = 688, p-value = 0.2297
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.02895491  0.12001039
## sample estimates:
##        cor 
## 0.04578225
cor.test(include$birth.pm10[include$childteen=="T"], include$peg.pm10.cent[include$childteen=="T"])
## 
##  Pearson's product-moment correlation
## 
## data:  include$birth.pm10[include$childteen == "T"] and include$peg.pm10.cent[include$childteen == "T"]
## t = 0.24723, df = 733, p-value = 0.8048
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.06322623  0.08139356
## sample estimates:
##         cor 
## 0.009131415
summary(include$birth.pm25)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   14.34   23.83   27.73   27.90   34.57   45.00
summary(include$peg.pm25.centstd)
##        V1          
##  Min.   :-1.96146  
##  1st Qu.:-0.58886  
##  Median :-0.11805  
##  Mean   :-0.05198  
##  3rd Qu.: 0.39186  
##  Max.   : 2.80857
cor.test(include$birth.pm25, include$peg.pm25.centstd)
## 
##  Pearson's product-moment correlation
## 
## data:  include$birth.pm25 and include$peg.pm25.centstd
## t = 1.505, df = 1540, p-value = 0.1325
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.01161822  0.08807393
## sample estimates:
##        cor 
## 0.03832321
p25 <- ggplot(include, aes(x=birth.pm25, y=peg.pm25.centstd)) +
  xlab("Prenatal Particulate Matter 2.5 uM Exposure (ug/m3/day)") +
  ylab("Standardized Saliva Poly DNA Methylation Score
      for Particulate Matter 2.5 uM Exposure") +
  geom_point() +
  geom_smooth(method=lm , color="dodgerblue", fill="#69b3a2", se=TRUE) +
  stat_cor(method= "pearson", label.x = 35) +
  theme_bw() 
p25
## `geom_smooth()` using formula 'y ~ x'

dim(include[include$childteen == "C",])
## [1] 749 222
p25.a9 <- ggplot(include[include$childteen == "C",], aes(x=birth.pm25, y=peg.pm25.centstd)) +
  xlab("Prenatal 2.5 uM Particulate Matter Exposure (ug/m3/day)") +
  ylab("Standardized Saliva Poly DNA Methylation Score
      for 2.5 uM Particulate Matter Exposure") +
  ggtitle("Age 9") +
  geom_point() +
  geom_smooth(method=lm , color="dodgerblue", fill="#69b3a2", se=TRUE) +
  stat_cor(method= "pearson", label.x = 35) +
  theme_bw() 
p25.a9
## `geom_smooth()` using formula 'y ~ x'

p25.a15 <- ggplot(include[include$childteen == "T",], aes(x=birth.pm25, y=peg.pm25.centstd)) +
  xlab("Prenatal 2.5 uM Particulate Matter Exposure (ug/m3/day)") +
  ylab("Standardized Saliva Poly DNA Methylation Score
      for 2.5 uM Particulate Matter Exposure") +
  ggtitle("Age 15") +
  geom_point() +
  geom_smooth(method=lm , color="dodgerblue", fill="#69b3a2", se=TRUE) +
  stat_cor(method= "pearson", label.x = 35) +
  theme_bw() 
p25.a15
## `geom_smooth()` using formula 'y ~ x'

p10 <- ggplot(include, aes(x=birth.pm10, y=peg.pm10.centstd)) +
  xlab("Prenatal 10 uM Particulate Matter Exposure (ug/m3/day)") +
  ylab("Standardized Saliva Poly DNA Methylation Score
      for 10 uM Particulate Matter Exposure") +
  geom_point() +
  geom_smooth(method=lm , color="dodgerblue", fill="#69b3a2", se=TRUE) +
    stat_cor(method= "pearson", label.x = 7) +
  theme_bw()

p10
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 117 rows containing non-finite values (stat_smooth).
## Warning: Removed 117 rows containing non-finite values (stat_cor).
## Warning: Removed 117 rows containing missing values (geom_point).

p10.a9 <- ggplot(include[include$childteen == "C",], aes(x=birth.pm10, y=peg.pm10.centstd)) +
  xlab("Prenatal 10 uM Particulate Matter Exposure (ug/m3/day)") +
  ylab("Standardized Saliva Poly DNA Methylation Score
      for 10 uM Particulate Matter Exposure") +
  ggtitle("Age 9") +
  geom_point() +
  geom_smooth(method=lm , color="dodgerblue", fill="#69b3a2", se=TRUE) +
  stat_cor(method= "pearson", label.x = 7) +
  theme_bw() 
p10.a9
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 59 rows containing non-finite values (stat_smooth).
## Warning: Removed 59 rows containing non-finite values (stat_cor).
## Warning: Removed 59 rows containing missing values (geom_point).

p10.a15 <- ggplot(include[include$childteen == "T",], aes(x=birth.pm10, y=peg.pm10.centstd)) +
  xlab("Prenatal 10 uM Particulate Matter Exposure (ug/m3/day)") +
  ylab("Standardized Saliva Poly DNA Methylation Score
      for 10 uM Particulate Matter Exposure") +
  ggtitle("Age 15") +
  geom_point() +
  geom_smooth(method=lm , color="dodgerblue", fill="#69b3a2", se=TRUE) +
  stat_cor(method= "pearson", label.x = 7) +
  theme_bw() 
p10.a15
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).

ggarrange(p25, p10, ncol=2)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 117 rows containing non-finite values (stat_smooth).
## Warning: Removed 117 rows containing non-finite values (stat_cor).
## Warning: Removed 117 rows containing missing values (geom_point).

pdf(file=here("Output",paste0("FFCW_AirPoll_PEGStd_Bivariate_Plots_", date, ".pdf")))
  p25
## `geom_smooth()` using formula 'y ~ x'
  p25.a9
## `geom_smooth()` using formula 'y ~ x'
  p25.a15
## `geom_smooth()` using formula 'y ~ x'
  p10
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 117 rows containing non-finite values (stat_smooth).
## Warning: Removed 117 rows containing non-finite values (stat_cor).
## Warning: Removed 117 rows containing missing values (geom_point).
  p10.a9
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 59 rows containing non-finite values (stat_smooth).
## Warning: Removed 59 rows containing non-finite values (stat_cor).
## Warning: Removed 59 rows containing missing values (geom_point).
  p10.a15
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).
dev.off()
## quartz_off_screen 
##                 2
p10.a15.1 <- ggplot(include[include$childteen == "T",], aes(x=birth.pm10, y=cg00905156.percent)) +
  xlab("Prenatal 10 uM Particulate Matter Exposure (ug/m3/day)") +
  ylab("Percent DNA Methylation at cg00905156") +
  ggtitle("Age 15 cg00905156") +
  geom_point() +
  geom_smooth(method=lm , color="dodgerblue", fill="#69b3a2", se=TRUE) +
  stat_cor(method= "pearson", label.x = 7) +
  theme_bw() 
p10.a15.1
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).

p10.a15.2 <- ggplot(include[include$childteen == "T",], aes(x=birth.pm10, y=cg06849931.percent)) +
  xlab("Prenatal 10 uM Particulate Matter Exposure (ug/m3/day)") +
  ylab("Percent DNA Methylation at cg06849931") +
  ggtitle("Age 15 cg06849931") +
  geom_point() +
  geom_smooth(method=lm , color="dodgerblue", fill="#69b3a2", se=TRUE) +
  stat_cor(method= "pearson", label.x = 7) +
  theme_bw() 
p10.a15.2
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).

p10.a15.3 <- ggplot(include[include$childteen == "T",], aes(x=birth.pm10, y=cg15082635.percent)) +
  xlab("Prenatal 10 uM Particulate Matter Exposure (ug/m3/day)") +
  ylab("Percent DNA Methylation at cg15082635") +
  ggtitle("Age 15 cg15082635") +
  geom_point() +
  geom_smooth(method=lm , color="dodgerblue", fill="#69b3a2", se=TRUE) +
  stat_cor(method= "pearson", label.x = 7) +
  theme_bw() 
p10.a15.3
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).

p10.a15.4 <- ggplot(include[include$childteen == "T",], aes(x=birth.pm10, y=cg18640183.percent)) +
  xlab("Prenatal 10 uM Particulate Matter Exposure (ug/m3/day)") +
  ylab("Percent DNA Methylation at cg18640183") +
  ggtitle("Age 15 cg18640183") +
  geom_point() +
  geom_smooth(method=lm , color="dodgerblue", fill="#69b3a2", se=TRUE) +
  stat_cor(method= "pearson", label.x = 7) +
  theme_bw() 
p10.a15.4
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).

p10.a15.5 <- ggplot(include[include$childteen == "T",], aes(x=birth.pm10, y=cg20340716.percent)) +
  xlab("Prenatal 10 uM Particulate Matter Exposure (ug/m3/day)") +
  ylab("Percent DNA Methylation at cg20340716") +
  ggtitle("Age 15 cg20340716") +
  geom_point() +
  geom_smooth(method=lm , color="dodgerblue", fill="#69b3a2", se=TRUE) +
  stat_cor(method= "pearson", label.x = 7) +
  theme_bw() 
p10.a15.5
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).

p10.a15.6 <- ggplot(include[include$childteen == "T",], aes(x=birth.pm10, y=cg24127244.percent)) +
  xlab("Prenatal 10 uM Particulate Matter Exposure (ug/m3/day)") +
  ylab("Percent DNA Methylation at cg24127244") +
  ggtitle("Age 15 cg24127244") +
  geom_point() +
  geom_smooth(method=lm , color="dodgerblue", fill="#69b3a2", se=TRUE) +
  stat_cor(method= "pearson", label.x = 7) +
  theme_bw() 
p10.a15.6
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).

#ggarrange(p25, p10, ncol=2)

pdf(file=here("Output",paste0("FFCW_AirPoll_SingleSitePM10_Bivariate_Plots_", date, ".pdf")))
  p10.a15.1
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).
  p10.a15.2
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).
  p10.a15.3
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).
  p10.a15.4
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).
  p10.a15.5
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).
  p10.a15.6
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 58 rows containing non-finite values (stat_smooth).
## Warning: Removed 58 rows containing non-finite values (stat_cor).
## Warning: Removed 58 rows containing missing values (geom_point).
dev.off()
## quartz_off_screen 
##                 2
mod.crude<-lm(peg.pm10.centstd ~ birth.pm10, data=include)
summary(mod.crude)

mod<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + ethrace.factor + cm1inpov + m1b2 + epithelial, data=include)
summary(mod)

mod.a9<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + ethrace.factor + cm1inpov + m1b2 + epithelial, data=include[include$childteen=="C",])
summary(mod.a9)

mod.a15<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + ethrace.factor + cm1inpov + m1b2 + epithelial, data=include[include$childteen=="T",])
summary(mod.a15)

mod.crude<-lm(peg.pm25.centstd ~ birth.pm25, data=include)
summary(mod.crude)

mod<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + ethrace.factor + cm1inpov + m1b2 + epithelial, data=include)
summary(mod)

mod.a9<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + ethrace.factor + cm1inpov + m1b2 + epithelial, data=include[include$childteen=="C",])
summary(mod.a9)

mod.a15<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + ethrace.factor + cm1inpov + m1b2 + epithelial, data=include[include$childteen=="T",])
summary(mod.a15)
output<-data.frame(matrix(nrow=6, ncol= 15))
colnames(output) <- c("Exposure", "Age", "N", "IQRcoef.r", "IQRlcl.r", "IQRucl.r", "pval.r",  "IQRcoef.c", "IQRlcl.c", "IQRucl.c", "pval.c",  "IQRcoef.cs", "IQRlcl.cs", "IQRucl.cs", "pval.cs")
output$Exposure <- c(rep("PM2.5", 3), rep("PM10", 3))
output$Age <- c("All", "9", "15")

IQR(include$birth.pm25, na.rm=T)
## [1] 10.74194
IQR(include$birth.pm10, na.rm=T)
## [1] 3.223546
### Raw

mod<-lm(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include)
summary(mod)
## 
## Call:
## lm(formula = peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.44351 -0.29124 -0.03297  0.23905  1.63541 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       6.218262   0.247480  25.126   <2e-16 ***
## birth.pm25                       -0.002444   0.001578  -1.549   0.1216    
## age.dnam                         -0.007199   0.003606  -1.996   0.0461 *  
## sexm                              0.048871   0.022109   2.210   0.0272 *  
## matrace.factorNon-Hispanic Black  0.454238   0.032763  13.864   <2e-16 ***
## matrace.factorHispanic            0.050338   0.037603   1.339   0.1809    
## matrace.factorOther               0.127801   0.064773   1.973   0.0487 *  
## cm1inpov                          0.008473   0.005263   1.610   0.1076    
## m1b2                              0.030455   0.030021   1.014   0.3105    
## Epithelial.cells                 -3.251741   0.229881 -14.145   <2e-16 ***
## Leukocytes                       -6.296349   0.214642 -29.334   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4313 on 1531 degrees of freedom
## Multiple R-squared:  0.6681, Adjusted R-squared:  0.6659 
## F-statistic: 308.2 on 10 and 1531 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="C",])
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "C", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.98221 -0.28743 -0.02908  0.24590  1.38465 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.445900   0.563023   9.673   <2e-16 ***
## birth.pm25                       -0.001956   0.002319  -0.844   0.3991    
## age.dnam                          0.062588   0.048379   1.294   0.1962    
## sexm                              0.056485   0.031230   1.809   0.0709 .  
## matrace.factorNon-Hispanic Black  0.456833   0.046336   9.859   <2e-16 ***
## matrace.factorHispanic            0.019871   0.053493   0.371   0.7104    
## matrace.factorOther               0.123495   0.091893   1.344   0.1794    
## cm1inpov                          0.012652   0.007461   1.696   0.0903 .  
## m1b2                              0.031438   0.042503   0.740   0.4597    
## Epithelial.cells                 -2.978967   0.336514  -8.852   <2e-16 ***
## Leukocytes                       -6.208579   0.310631 -19.987   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4245 on 738 degrees of freedom
## Multiple R-squared:  0.6516, Adjusted R-squared:  0.6469 
## F-statistic:   138 on 10 and 738 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="T",])
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "T", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.43336 -0.28655 -0.03036  0.23866  1.59827 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.396585   0.607028   8.890   <2e-16 ***
## birth.pm25                       -0.001611   0.002255  -0.715    0.475    
## age.dnam                          0.052836   0.032621   1.620    0.106    
## sexm                              0.039777   0.031381   1.268    0.205    
## matrace.factorNon-Hispanic Black  0.449189   0.046662   9.626   <2e-16 ***
## matrace.factorHispanic            0.064485   0.053675   1.201    0.230    
## matrace.factorOther               0.142255   0.091394   1.557    0.120    
## cm1inpov                          0.007008   0.007498   0.935    0.350    
## m1b2                              0.039755   0.042579   0.934    0.351    
## Epithelial.cells                 -3.488749   0.318502 -10.954   <2e-16 ***
## Leukocytes                       -6.416369   0.299543 -21.421   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4376 on 782 degrees of freedom
## Multiple R-squared:  0.6842, Adjusted R-squared:  0.6802 
## F-statistic: 169.5 on 10 and 782 DF,  p-value: < 2.2e-16
output$N[1:3] <- c(nrow(model.frame(mod)), nrow(model.frame(mod.a9)), nrow(model.frame(mod.a15)))

output$IQRcoef.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.r[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include)
summary(mod)
## 
## Call:
## lm(formula = peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6581 -0.1420 -0.0116  0.1233  1.1677 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.521334   0.132892  26.498  < 2e-16 ***
## birth.pm10                       -0.004215   0.002109  -1.999  0.04584 *  
## age.dnam                          0.005281   0.001906   2.771  0.00567 ** 
## sexm                              0.029846   0.011625   2.567  0.01035 *  
## matrace.factorNon-Hispanic Black  0.003325   0.016922   0.197  0.84423    
## matrace.factorHispanic           -0.028031   0.020308  -1.380  0.16772    
## matrace.factorOther              -0.068369   0.034268  -1.995  0.04622 *  
## cm1inpov                         -0.002297   0.002749  -0.836  0.40344    
## m1b2                             -0.011871   0.016037  -0.740  0.45930    
## Epithelial.cells                 -0.636195   0.121404  -5.240 1.85e-07 ***
## Leukocytes                       -3.557948   0.113289 -31.406  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2176 on 1414 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.8204, Adjusted R-squared:  0.8191 
## F-statistic: 645.8 on 10 and 1414 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="C",])
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "C", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63216 -0.15057 -0.01596  0.12294  1.18076 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.214426   0.315658  10.183  < 2e-16 ***
## birth.pm10                       -0.001163   0.003030  -0.384  0.70135    
## age.dnam                          0.030199   0.027873   1.083  0.27899    
## sexm                              0.020818   0.016847   1.236  0.21699    
## matrace.factorNon-Hispanic Black  0.023042   0.024540   0.939  0.34807    
## matrace.factorHispanic           -0.004747   0.029416  -0.161  0.87186    
## matrace.factorOther              -0.052276   0.050386  -1.038  0.29987    
## cm1inpov                         -0.001332   0.003995  -0.333  0.73896    
## m1b2                             -0.029158   0.023278  -1.253  0.21078    
## Epithelial.cells                 -0.610568   0.181101  -3.371  0.00079 ***
## Leukocytes                       -3.512162   0.166771 -21.060  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2196 on 679 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.782,  Adjusted R-squared:  0.7788 
## F-statistic: 243.6 on 10 and 679 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="T",])
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "T", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.60698 -0.13903 -0.00462  0.12867  1.18525 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.207552   0.350745   9.145  < 2e-16 ***
## birth.pm10                       -0.007504   0.002985  -2.514   0.0122 *  
## age.dnam                          0.032467   0.020208   1.607   0.1086    
## sexm                              0.036896   0.016153   2.284   0.0226 *  
## matrace.factorNon-Hispanic Black -0.016198   0.023518  -0.689   0.4912    
## matrace.factorHispanic           -0.053499   0.028225  -1.895   0.0584 .  
## matrace.factorOther              -0.079437   0.046903  -1.694   0.0908 .  
## cm1inpov                         -0.002633   0.003818  -0.690   0.4907    
## m1b2                              0.004812   0.022182   0.217   0.8283    
## Epithelial.cells                 -0.689189   0.165184  -4.172 3.38e-05 ***
## Leukocytes                       -3.624538   0.155364 -23.329  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2161 on 724 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.8438, Adjusted R-squared:  0.8416 
## F-statistic:   391 on 10 and 724 DF,  p-value: < 2.2e-16
output$N[4:6] <- c(nrow(model.frame(mod)), nrow(model.frame(mod.a9)), nrow(model.frame(mod.a15)))

output$IQRcoef.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.r[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Centered

mod<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include)
summary(mod)
## 
## Call:
## lm(formula = peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.44351 -0.29124 -0.03297  0.23905  1.63541 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       6.218262   0.247480  25.126   <2e-16 ***
## birth.pm25                       -0.002444   0.001578  -1.549   0.1216    
## age.dnam                         -0.007199   0.003606  -1.996   0.0461 *  
## sexm                              0.048871   0.022109   2.210   0.0272 *  
## matrace.factorNon-Hispanic Black  0.454238   0.032763  13.864   <2e-16 ***
## matrace.factorHispanic            0.050338   0.037603   1.339   0.1809    
## matrace.factorOther               0.127801   0.064773   1.973   0.0487 *  
## cm1inpov                          0.008473   0.005263   1.610   0.1076    
## m1b2                              0.030455   0.030021   1.014   0.3105    
## Epithelial.cells                 -3.251741   0.229881 -14.145   <2e-16 ***
## Leukocytes                       -6.296349   0.214642 -29.334   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4313 on 1531 degrees of freedom
## Multiple R-squared:  0.6681, Adjusted R-squared:  0.6659 
## F-statistic: 308.2 on 10 and 1531 DF,  p-value: < 2.2e-16
summary(mod)$coef[2,4]
## [1] 0.1215543
mod.a9<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="C",])
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "C", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.98221 -0.28743 -0.02908  0.24590  1.38465 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.445900   0.563023   9.673   <2e-16 ***
## birth.pm25                       -0.001956   0.002319  -0.844   0.3991    
## age.dnam                          0.062588   0.048379   1.294   0.1962    
## sexm                              0.056485   0.031230   1.809   0.0709 .  
## matrace.factorNon-Hispanic Black  0.456833   0.046336   9.859   <2e-16 ***
## matrace.factorHispanic            0.019871   0.053493   0.371   0.7104    
## matrace.factorOther               0.123495   0.091893   1.344   0.1794    
## cm1inpov                          0.012652   0.007461   1.696   0.0903 .  
## m1b2                              0.031438   0.042503   0.740   0.4597    
## Epithelial.cells                 -2.978967   0.336514  -8.852   <2e-16 ***
## Leukocytes                       -6.208579   0.310631 -19.987   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4245 on 738 degrees of freedom
## Multiple R-squared:  0.6516, Adjusted R-squared:  0.6469 
## F-statistic:   138 on 10 and 738 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="T",])
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "T", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.43336 -0.28655 -0.03036  0.23866  1.59827 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.396585   0.607028   8.890   <2e-16 ***
## birth.pm25                       -0.001611   0.002255  -0.715    0.475    
## age.dnam                          0.052836   0.032621   1.620    0.106    
## sexm                              0.039777   0.031381   1.268    0.205    
## matrace.factorNon-Hispanic Black  0.449189   0.046662   9.626   <2e-16 ***
## matrace.factorHispanic            0.064485   0.053675   1.201    0.230    
## matrace.factorOther               0.142255   0.091394   1.557    0.120    
## cm1inpov                          0.007008   0.007498   0.935    0.350    
## m1b2                              0.039755   0.042579   0.934    0.351    
## Epithelial.cells                 -3.488749   0.318502 -10.954   <2e-16 ***
## Leukocytes                       -6.416369   0.299543 -21.421   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4376 on 782 degrees of freedom
## Multiple R-squared:  0.6842, Adjusted R-squared:  0.6802 
## F-statistic: 169.5 on 10 and 782 DF,  p-value: < 2.2e-16
output$IQRcoef.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.c[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include)
summary(mod)
## 
## Call:
## lm(formula = peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6581 -0.1420 -0.0116  0.1233  1.1677 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.521334   0.132892  26.498  < 2e-16 ***
## birth.pm10                       -0.004215   0.002109  -1.999  0.04584 *  
## age.dnam                          0.005281   0.001906   2.771  0.00567 ** 
## sexm                              0.029846   0.011625   2.567  0.01035 *  
## matrace.factorNon-Hispanic Black  0.003325   0.016922   0.197  0.84423    
## matrace.factorHispanic           -0.028031   0.020308  -1.380  0.16772    
## matrace.factorOther              -0.068369   0.034268  -1.995  0.04622 *  
## cm1inpov                         -0.002297   0.002749  -0.836  0.40344    
## m1b2                             -0.011871   0.016037  -0.740  0.45930    
## Epithelial.cells                 -0.636195   0.121404  -5.240 1.85e-07 ***
## Leukocytes                       -3.557948   0.113289 -31.406  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2176 on 1414 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.8204, Adjusted R-squared:  0.8191 
## F-statistic: 645.8 on 10 and 1414 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="C",])
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "C", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63216 -0.15057 -0.01596  0.12294  1.18076 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.214426   0.315658  10.183  < 2e-16 ***
## birth.pm10                       -0.001163   0.003030  -0.384  0.70135    
## age.dnam                          0.030199   0.027873   1.083  0.27899    
## sexm                              0.020818   0.016847   1.236  0.21699    
## matrace.factorNon-Hispanic Black  0.023042   0.024540   0.939  0.34807    
## matrace.factorHispanic           -0.004747   0.029416  -0.161  0.87186    
## matrace.factorOther              -0.052276   0.050386  -1.038  0.29987    
## cm1inpov                         -0.001332   0.003995  -0.333  0.73896    
## m1b2                             -0.029158   0.023278  -1.253  0.21078    
## Epithelial.cells                 -0.610568   0.181101  -3.371  0.00079 ***
## Leukocytes                       -3.512162   0.166771 -21.060  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2196 on 679 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.782,  Adjusted R-squared:  0.7788 
## F-statistic: 243.6 on 10 and 679 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="T",])
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "T", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.60698 -0.13903 -0.00462  0.12867  1.18525 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.207552   0.350745   9.145  < 2e-16 ***
## birth.pm10                       -0.007504   0.002985  -2.514   0.0122 *  
## age.dnam                          0.032467   0.020208   1.607   0.1086    
## sexm                              0.036896   0.016153   2.284   0.0226 *  
## matrace.factorNon-Hispanic Black -0.016198   0.023518  -0.689   0.4912    
## matrace.factorHispanic           -0.053499   0.028225  -1.895   0.0584 .  
## matrace.factorOther              -0.079437   0.046903  -1.694   0.0908 .  
## cm1inpov                         -0.002633   0.003818  -0.690   0.4907    
## m1b2                              0.004812   0.022182   0.217   0.8283    
## Epithelial.cells                 -0.689189   0.165184  -4.172 3.38e-05 ***
## Leukocytes                       -3.624538   0.155364 -23.329  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2161 on 724 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.8438, Adjusted R-squared:  0.8416 
## F-statistic:   391 on 10 and 724 DF,  p-value: < 2.2e-16
output$IQRcoef.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.c[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Centered & Scaled

mod<-lm(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include)
summary(mod)
## 
## Call:
## lm(formula = peg.pm25.centscalestd ~ birth.pm25 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes, data = include)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0604 -0.2445 -0.0272  0.2026  1.3866 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       4.856240   0.202688  23.959  < 2e-16 ***
## birth.pm25                       -0.001599   0.001292  -1.238  0.21592    
## age.dnam                         -0.008936   0.002954  -3.025  0.00252 ** 
## sexm                              0.039235   0.018108   2.167  0.03041 *  
## matrace.factorNon-Hispanic Black  0.277397   0.026833  10.338  < 2e-16 ***
## matrace.factorHispanic            0.043612   0.030797   1.416  0.15694    
## matrace.factorOther               0.073626   0.053050   1.388  0.16538    
## cm1inpov                          0.008217   0.004310   1.906  0.05678 .  
## m1b2                              0.019549   0.024587   0.795  0.42669    
## Epithelial.cells                 -3.096543   0.188274 -16.447  < 2e-16 ***
## Leukocytes                       -4.829100   0.175793 -27.470  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3532 on 1531 degrees of freedom
## Multiple R-squared:  0.5489, Adjusted R-squared:  0.5459 
## F-statistic: 186.3 on 10 and 1531 DF,  p-value: < 2.2e-16
summary(mod)$coef[2,4]
## [1] 0.2159189
mod.a9<-lm(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="C",])
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm25.centscalestd ~ birth.pm25 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes, data = include[include$childteen == "C", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.88105 -0.23885 -0.02792  0.18658  1.40587 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       4.128178   0.460774   8.959  < 2e-16 ***
## birth.pm25                       -0.001347   0.001898  -0.710   0.4782    
## age.dnam                          0.050602   0.039593   1.278   0.2016    
## sexm                              0.056243   0.025559   2.201   0.0281 *  
## matrace.factorNon-Hispanic Black  0.287153   0.037921   7.572  1.1e-13 ***
## matrace.factorHispanic            0.030224   0.043778   0.690   0.4902    
## matrace.factorOther               0.064207   0.075205   0.854   0.3935    
## cm1inpov                          0.012208   0.006106   1.999   0.0459 *  
## m1b2                              0.012192   0.034784   0.351   0.7261    
## Epithelial.cells                 -2.796880   0.275401 -10.156  < 2e-16 ***
## Leukocytes                       -4.683960   0.254219 -18.425  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3474 on 738 degrees of freedom
## Multiple R-squared:  0.5377, Adjusted R-squared:  0.5314 
## F-statistic: 85.82 on 10 and 738 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="T",])
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm25.centscalestd ~ birth.pm25 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes, data = include[include$childteen == "T", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.07010 -0.25322 -0.02487  0.20100  1.31323 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       4.3276708  0.4971293   8.705  < 2e-16 ***
## birth.pm25                       -0.0007746  0.0018464  -0.419    0.675    
## age.dnam                          0.0349958  0.0267149   1.310    0.191    
## sexm                              0.0222679  0.0256993   0.866    0.386    
## matrace.factorNon-Hispanic Black  0.2669193  0.0382142   6.985  6.1e-12 ***
## matrace.factorHispanic            0.0437015  0.0439575   0.994    0.320    
## matrace.factorOther               0.0902787  0.0748477   1.206    0.228    
## cm1inpov                          0.0062972  0.0061405   1.026    0.305    
## m1b2                              0.0345343  0.0348705   0.990    0.322    
## Epithelial.cells                 -3.3561291  0.2608390 -12.867  < 2e-16 ***
## Leukocytes                       -4.9919163  0.2453121 -20.349  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3584 on 782 degrees of freedom
## Multiple R-squared:  0.5646, Adjusted R-squared:  0.559 
## F-statistic: 101.4 on 10 and 782 DF,  p-value: < 2.2e-16
output$IQRcoef.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.cs[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.pm10.centscalestd ~  birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include)
summary(mod)
## 
## Call:
## lm(formula = peg.pm10.centscalestd ~ birth.pm10 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes, data = include)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.51361 -0.11775 -0.02182  0.09471  1.58211 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       0.831740   0.113196   7.348 3.39e-13 ***
## birth.pm10                       -0.004009   0.001796  -2.232   0.0258 *  
## age.dnam                          0.007468   0.001623   4.600 4.60e-06 ***
## sexm                              0.015967   0.009902   1.612   0.1071    
## matrace.factorNon-Hispanic Black -0.009984   0.014414  -0.693   0.4886    
## matrace.factorHispanic           -0.031905   0.017298  -1.844   0.0653 .  
## matrace.factorOther              -0.065540   0.029189  -2.245   0.0249 *  
## cm1inpov                         -0.002311   0.002341  -0.987   0.3239    
## m1b2                             -0.011956   0.013660  -0.875   0.3816    
## Epithelial.cells                 -0.152903   0.103410  -1.479   0.1395    
## Leukocytes                       -0.912124   0.096498  -9.452  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1853 on 1414 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.3139, Adjusted R-squared:  0.3091 
## F-statistic:  64.7 on 10 and 1414 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="C",])
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm10.centscalestd ~ birth.pm10 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes, data = include[include$childteen == "C", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.49250 -0.11919 -0.01830  0.09186  1.46095 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       6.716e-01  2.703e-01   2.485   0.0132 *  
## birth.pm10                       -1.462e-03  2.594e-03  -0.563   0.5733    
## age.dnam                          8.963e-05  2.386e-02   0.004   0.9970    
## sexm                              1.515e-02  1.442e-02   1.050   0.2940    
## matrace.factorNon-Hispanic Black  1.451e-02  2.101e-02   0.691   0.4901    
## matrace.factorHispanic            1.855e-03  2.519e-02   0.074   0.9413    
## matrace.factorOther              -4.722e-02  4.314e-02  -1.095   0.2740    
## cm1inpov                         -1.634e-03  3.421e-03  -0.478   0.6330    
## m1b2                             -3.175e-02  1.993e-02  -1.593   0.1117    
## Epithelial.cells                  4.501e-02  1.551e-01   0.290   0.7717    
## Leukocytes                       -7.221e-01  1.428e-01  -5.057 5.49e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.188 on 679 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.2444, Adjusted R-squared:  0.2333 
## F-statistic: 21.96 on 10 and 679 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="T",])
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm10.centscalestd ~ birth.pm10 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes, data = include[include$childteen == "T", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.45742 -0.11813 -0.01366  0.09609  1.60613 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       0.756329   0.296373   2.552  0.01092 *  
## birth.pm10                       -0.007128   0.002523  -2.826  0.00485 ** 
## age.dnam                          0.027182   0.017076   1.592  0.11186    
## sexm                              0.016387   0.013649   1.201  0.23029    
## matrace.factorNon-Hispanic Black -0.033345   0.019872  -1.678  0.09378 .  
## matrace.factorHispanic           -0.065824   0.023850  -2.760  0.00593 ** 
## matrace.factorOther              -0.080313   0.039632  -2.026  0.04309 *  
## cm1inpov                         -0.002926   0.003226  -0.907  0.36475    
## m1b2                              0.006058   0.018744   0.323  0.74664    
## Epithelial.cells                 -0.334108   0.139577  -2.394  0.01693 *  
## Leukocytes                       -1.091013   0.131280  -8.311 4.69e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1826 on 724 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.352,  Adjusted R-squared:  0.343 
## F-statistic: 39.32 on 10 and 724 DF,  p-value: < 2.2e-16
output$IQRcoef.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.cs[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Output

output
##   Exposure Age    N    IQRcoef.r    IQRlcl.r      IQRucl.r     pval.r
## 1    PM2.5 All 1542 -0.026251799 -0.05949168  0.0069880783 0.12155427
## 2    PM2.5   9  749 -0.021015016 -0.06991742  0.0278873883 0.39914041
## 3    PM2.5  15  793 -0.017308406 -0.06484958  0.0302327723 0.47502386
## 4     PM10 All 1425 -0.013587089 -0.02692297 -0.0002512052 0.04584345
## 5     PM10   9  690 -0.003747442 -0.02292578  0.0154308944 0.70135034
## 6     PM10  15  735 -0.024189848 -0.04308380 -0.0052958947 0.01216875
##      IQRcoef.c    IQRlcl.c      IQRucl.c     pval.c   IQRcoef.cs   IQRlcl.cs
## 1 -0.026251799 -0.05949168  0.0069880783 0.12155427 -0.017181587 -0.04440525
## 2 -0.021015016 -0.06991742  0.0278873883 0.39914041 -0.014465622 -0.05448704
## 3 -0.017308406 -0.06484958  0.0302327723 0.47502386 -0.008320188 -0.04725430
## 4 -0.013587089 -0.02692297 -0.0002512052 0.04584345 -0.012923461 -0.02428282
## 5 -0.003747442 -0.02292578  0.0154308944 0.70135034 -0.004711884 -0.02113217
## 6 -0.024189848 -0.04308380 -0.0052958947 0.01216875 -0.022978486 -0.03894355
##      IQRucl.cs     pval.cs
## 1  0.010042071 0.215918859
## 2  0.025555800 0.478184525
## 3  0.030613925 0.674971855
## 4 -0.001564100 0.025787884
## 5  0.011708397 0.573330789
## 6 -0.007013419 0.004847766
write.csv(output, file=here("Output",paste0("FFCW_AirPoll_Std_PEG_Regression_Primary_", date, ".csv")) )
output<-data.frame(matrix(nrow=6, ncol= 15))
colnames(output) <- c("Exposure", "Age", "N", "IQRcoef.r", "IQRlcl.r", "IQRucl.r", "pval.r",  "IQRcoef.c", "IQRlcl.c", "IQRucl.c", "pval.c",  "IQRcoef.cs", "IQRlcl.cs", "IQRucl.cs", "pval.cs")
output$Exposure <- c(rep("PM2.5", 3), rep("PM10", 3))
output$Age <- c("All", "9", "15")

IQR(include$birth.pm25, na.rm=T)
## [1] 10.74194
IQR(include$birth.pm10, na.rm=T)
## [1] 3.223546
### Raw

mod<-lm(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm25, na.option=na.exclude, data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age1.pm25, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.45457 -0.28999 -0.02866  0.24918  1.63847 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       6.1200338  0.2569811  23.815   <2e-16 ***
## birth.pm25                       -0.0015071  0.0018974  -0.794   0.4271    
## age.dnam                         -0.0075334  0.0036622  -2.057   0.0399 *  
## sexm                              0.0481505  0.0225181   2.138   0.0327 *  
## matrace.factorNon-Hispanic Black  0.4467164  0.0332790  13.423   <2e-16 ***
## matrace.factorHispanic            0.0353149  0.0381392   0.926   0.3546    
## matrace.factorOther               0.1510251  0.0666669   2.265   0.0236 *  
## cm1inpov                          0.0076122  0.0053265   1.429   0.1532    
## m1b2                              0.0436190  0.0303998   1.435   0.1515    
## Epithelial.cells                 -3.1208299  0.2366049 -13.190   <2e-16 ***
## Leukocytes                       -6.2273754  0.2199940 -28.307   <2e-16 ***
## age1.pm25                        -0.0004308  0.0025437  -0.169   0.8655    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4258 on 1442 degrees of freedom
##   (88 observations deleted due to missingness)
## Multiple R-squared:  0.6689, Adjusted R-squared:  0.6664 
## F-statistic: 264.8 on 11 and 1442 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm25, na.option=na.exclude,  data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age1.pm25, data = include[include$childteen == "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.99652 -0.28614 -0.02787  0.25552  1.38105 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.2818173  0.5892193   8.964  < 2e-16 ***
## birth.pm25                       -0.0009422  0.0027535  -0.342   0.7323    
## age.dnam                          0.0717139  0.0505174   1.420   0.1562    
## sexm                              0.0547257  0.0318216   1.720   0.0859 .  
## matrace.factorNon-Hispanic Black  0.4519049  0.0470859   9.597  < 2e-16 ***
## matrace.factorHispanic           -0.0004930  0.0545214  -0.009   0.9928    
## matrace.factorOther               0.1487114  0.0950118   1.565   0.1180    
## cm1inpov                          0.0110326  0.0075445   1.462   0.1441    
## m1b2                              0.0426406  0.0431246   0.989   0.3231    
## Epithelial.cells                 -2.9257552  0.3499135  -8.361 3.36e-16 ***
## Leukocytes                       -6.1380133  0.3211714 -19.111  < 2e-16 ***
## age1.pm25                        -0.0010383  0.0035812  -0.290   0.7720    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4205 on 698 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.6542, Adjusted R-squared:  0.6487 
## F-statistic:   120 on 11 and 698 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm25, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age1.pm25, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.44032 -0.28526 -0.02869  0.24546  1.60912 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.290e+00  6.260e-01   8.452   <2e-16 ***
## birth.pm25                       -5.438e-04  2.711e-03  -0.201   0.8411    
## age.dnam                          5.106e-02  3.350e-02   1.524   0.1279    
## sexm                              4.103e-02  3.205e-02   1.280   0.2009    
## matrace.factorNon-Hispanic Black  4.360e-01  4.748e-02   9.183   <2e-16 ***
## matrace.factorHispanic            5.129e-02  5.453e-02   0.941   0.3472    
## matrace.factorOther               1.630e-01  9.394e-02   1.735   0.0832 .  
## cm1inpov                          6.843e-03  7.602e-03   0.900   0.3683    
## m1b2                              5.176e-02  4.308e-02   1.202   0.2299    
## Epithelial.cells                 -3.306e+00  3.259e-01 -10.146   <2e-16 ***
## Leukocytes                       -6.336e+00  3.059e-01 -20.713   <2e-16 ***
## age1.pm25                         9.181e-05  3.627e-03   0.025   0.9798    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4318 on 732 degrees of freedom
##   (49 observations deleted due to missingness)
## Multiple R-squared:  0.6836, Adjusted R-squared:  0.6788 
## F-statistic: 143.8 on 11 and 732 DF,  p-value: < 2.2e-16
output$N[1:3] <- c(nrow(model.frame(mod)), nrow(model.frame(mod.a9)), nrow(model.frame(mod.a15)))

output$IQRcoef.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.r[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm10, na.option=na.exclude,  data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age1.pm10, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.66580 -0.14005 -0.01082  0.12195  1.16239 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.5376054  0.1396473  25.332  < 2e-16 ***
## birth.pm10                       -0.0069373  0.0028710  -2.416  0.01581 *  
## age.dnam                          0.0052423  0.0019649   2.668  0.00772 ** 
## sexm                              0.0260678  0.0120074   2.171  0.03011 *  
## matrace.factorNon-Hispanic Black  0.0003462  0.0173743   0.020  0.98410    
## matrace.factorHispanic           -0.0303513  0.0210471  -1.442  0.14952    
## matrace.factorOther              -0.0609405  0.0357689  -1.704  0.08867 .  
## cm1inpov                         -0.0033609  0.0028034  -1.199  0.23080    
## m1b2                             -0.0126966  0.0164759  -0.771  0.44107    
## Epithelial.cells                 -0.6445176  0.1265619  -5.093 4.04e-07 ***
## Leukocytes                       -3.5732882  0.1177538 -30.345  < 2e-16 ***
## age1.pm10                         0.0033933  0.0030477   1.113  0.26574    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.218 on 1330 degrees of freedom
##   (200 observations deleted due to missingness)
## Multiple R-squared:  0.8132, Adjusted R-squared:  0.8117 
## F-statistic: 526.4 on 11 and 1330 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm10, na.option=na.exclude,  data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age1.pm10, data = include[include$childteen == "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.60815 -0.15025 -0.01492  0.11869  1.17565 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.198306   0.338641   9.445  < 2e-16 ***
## birth.pm10                       -0.004441   0.004131  -1.075 0.282755    
## age.dnam                          0.040219   0.030175   1.333 0.183049    
## sexm                              0.017769   0.017417   1.020 0.308027    
## matrace.factorNon-Hispanic Black  0.021987   0.025217   0.872 0.383575    
## matrace.factorHispanic           -0.007464   0.030626  -0.244 0.807533    
## matrace.factorOther              -0.040892   0.052973  -0.772 0.440438    
## cm1inpov                         -0.002476   0.004074  -0.608 0.543466    
## m1b2                             -0.025919   0.024031  -1.079 0.281180    
## Epithelial.cells                 -0.720407   0.190581  -3.780 0.000171 ***
## Leukocytes                       -3.592974   0.174261 -20.618  < 2e-16 ***
## age1.pm10                         0.004130   0.004416   0.935 0.349953    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2208 on 641 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.7747, Adjusted R-squared:  0.7708 
## F-statistic: 200.4 on 11 and 641 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm10, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age1.pm10, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63179 -0.13738 -0.00687  0.12499  1.17605 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.2330885  0.3637007   8.889  < 2e-16 ***
## birth.pm10                       -0.0093624  0.0040211  -2.328 0.020190 *  
## age.dnam                          0.0285585  0.0208705   1.368 0.171650    
## sexm                              0.0326116  0.0166896   1.954 0.051112 .  
## matrace.factorNon-Hispanic Black -0.0221496  0.0241286  -0.918 0.358956    
## matrace.factorHispanic           -0.0576458  0.0292231  -1.973 0.048946 *  
## matrace.factorOther              -0.0767131  0.0487136  -1.575 0.115775    
## cm1inpov                         -0.0036416  0.0038964  -0.935 0.350326    
## m1b2                              0.0003721  0.0227086   0.016 0.986933    
## Epithelial.cells                 -0.6148671  0.1709608  -3.597 0.000346 ***
## Leukocytes                       -3.5844191  0.1609197 -22.275  < 2e-16 ***
## age1.pm10                         0.0023906  0.0042399   0.564 0.573057    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.216 on 677 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.8374, Adjusted R-squared:  0.8348 
## F-statistic:   317 on 11 and 677 DF,  p-value: < 2.2e-16
output$N[4:6] <- c(nrow(model.frame(mod)), nrow(model.frame(mod.a9)), nrow(model.frame(mod.a15)))

output$IQRcoef.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.r[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Centered

mod<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm25, na.option=na.exclude,  data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age1.pm25, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.45457 -0.28999 -0.02866  0.24918  1.63847 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       6.1200338  0.2569811  23.815   <2e-16 ***
## birth.pm25                       -0.0015071  0.0018974  -0.794   0.4271    
## age.dnam                         -0.0075334  0.0036622  -2.057   0.0399 *  
## sexm                              0.0481505  0.0225181   2.138   0.0327 *  
## matrace.factorNon-Hispanic Black  0.4467164  0.0332790  13.423   <2e-16 ***
## matrace.factorHispanic            0.0353149  0.0381392   0.926   0.3546    
## matrace.factorOther               0.1510251  0.0666669   2.265   0.0236 *  
## cm1inpov                          0.0076122  0.0053265   1.429   0.1532    
## m1b2                              0.0436190  0.0303998   1.435   0.1515    
## Epithelial.cells                 -3.1208299  0.2366049 -13.190   <2e-16 ***
## Leukocytes                       -6.2273754  0.2199940 -28.307   <2e-16 ***
## age1.pm25                        -0.0004308  0.0025437  -0.169   0.8655    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4258 on 1442 degrees of freedom
##   (88 observations deleted due to missingness)
## Multiple R-squared:  0.6689, Adjusted R-squared:  0.6664 
## F-statistic: 264.8 on 11 and 1442 DF,  p-value: < 2.2e-16
summary(mod)$coef[2,4]
## [1] 0.4271485
mod.a9<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm25, na.option=na.exclude,  data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age1.pm25, data = include[include$childteen == "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.99652 -0.28614 -0.02787  0.25552  1.38105 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.2818173  0.5892193   8.964  < 2e-16 ***
## birth.pm25                       -0.0009422  0.0027535  -0.342   0.7323    
## age.dnam                          0.0717139  0.0505174   1.420   0.1562    
## sexm                              0.0547257  0.0318216   1.720   0.0859 .  
## matrace.factorNon-Hispanic Black  0.4519049  0.0470859   9.597  < 2e-16 ***
## matrace.factorHispanic           -0.0004930  0.0545214  -0.009   0.9928    
## matrace.factorOther               0.1487114  0.0950118   1.565   0.1180    
## cm1inpov                          0.0110326  0.0075445   1.462   0.1441    
## m1b2                              0.0426406  0.0431246   0.989   0.3231    
## Epithelial.cells                 -2.9257552  0.3499135  -8.361 3.36e-16 ***
## Leukocytes                       -6.1380133  0.3211714 -19.111  < 2e-16 ***
## age1.pm25                        -0.0010383  0.0035812  -0.290   0.7720    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4205 on 698 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.6542, Adjusted R-squared:  0.6487 
## F-statistic:   120 on 11 and 698 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm25, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age1.pm25, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.44032 -0.28526 -0.02869  0.24546  1.60912 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.290e+00  6.260e-01   8.452   <2e-16 ***
## birth.pm25                       -5.438e-04  2.711e-03  -0.201   0.8411    
## age.dnam                          5.106e-02  3.350e-02   1.524   0.1279    
## sexm                              4.103e-02  3.205e-02   1.280   0.2009    
## matrace.factorNon-Hispanic Black  4.360e-01  4.748e-02   9.183   <2e-16 ***
## matrace.factorHispanic            5.129e-02  5.453e-02   0.941   0.3472    
## matrace.factorOther               1.630e-01  9.394e-02   1.735   0.0832 .  
## cm1inpov                          6.843e-03  7.602e-03   0.900   0.3683    
## m1b2                              5.176e-02  4.308e-02   1.202   0.2299    
## Epithelial.cells                 -3.306e+00  3.259e-01 -10.146   <2e-16 ***
## Leukocytes                       -6.336e+00  3.059e-01 -20.713   <2e-16 ***
## age1.pm25                         9.181e-05  3.627e-03   0.025   0.9798    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4318 on 732 degrees of freedom
##   (49 observations deleted due to missingness)
## Multiple R-squared:  0.6836, Adjusted R-squared:  0.6788 
## F-statistic: 143.8 on 11 and 732 DF,  p-value: < 2.2e-16
output$IQRcoef.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.c[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm10, na.option=na.exclude, data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age1.pm10, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.66580 -0.14005 -0.01082  0.12195  1.16239 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.5376054  0.1396473  25.332  < 2e-16 ***
## birth.pm10                       -0.0069373  0.0028710  -2.416  0.01581 *  
## age.dnam                          0.0052423  0.0019649   2.668  0.00772 ** 
## sexm                              0.0260678  0.0120074   2.171  0.03011 *  
## matrace.factorNon-Hispanic Black  0.0003462  0.0173743   0.020  0.98410    
## matrace.factorHispanic           -0.0303513  0.0210471  -1.442  0.14952    
## matrace.factorOther              -0.0609405  0.0357689  -1.704  0.08867 .  
## cm1inpov                         -0.0033609  0.0028034  -1.199  0.23080    
## m1b2                             -0.0126966  0.0164759  -0.771  0.44107    
## Epithelial.cells                 -0.6445176  0.1265619  -5.093 4.04e-07 ***
## Leukocytes                       -3.5732882  0.1177538 -30.345  < 2e-16 ***
## age1.pm10                         0.0033933  0.0030477   1.113  0.26574    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.218 on 1330 degrees of freedom
##   (200 observations deleted due to missingness)
## Multiple R-squared:  0.8132, Adjusted R-squared:  0.8117 
## F-statistic: 526.4 on 11 and 1330 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm10, na.option=na.exclude, data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age1.pm10, data = include[include$childteen == "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.60815 -0.15025 -0.01492  0.11869  1.17565 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.198306   0.338641   9.445  < 2e-16 ***
## birth.pm10                       -0.004441   0.004131  -1.075 0.282755    
## age.dnam                          0.040219   0.030175   1.333 0.183049    
## sexm                              0.017769   0.017417   1.020 0.308027    
## matrace.factorNon-Hispanic Black  0.021987   0.025217   0.872 0.383575    
## matrace.factorHispanic           -0.007464   0.030626  -0.244 0.807533    
## matrace.factorOther              -0.040892   0.052973  -0.772 0.440438    
## cm1inpov                         -0.002476   0.004074  -0.608 0.543466    
## m1b2                             -0.025919   0.024031  -1.079 0.281180    
## Epithelial.cells                 -0.720407   0.190581  -3.780 0.000171 ***
## Leukocytes                       -3.592974   0.174261 -20.618  < 2e-16 ***
## age1.pm10                         0.004130   0.004416   0.935 0.349953    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2208 on 641 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.7747, Adjusted R-squared:  0.7708 
## F-statistic: 200.4 on 11 and 641 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm10, na.option=na.exclude, data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age1.pm10, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63179 -0.13738 -0.00687  0.12499  1.17605 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.2330885  0.3637007   8.889  < 2e-16 ***
## birth.pm10                       -0.0093624  0.0040211  -2.328 0.020190 *  
## age.dnam                          0.0285585  0.0208705   1.368 0.171650    
## sexm                              0.0326116  0.0166896   1.954 0.051112 .  
## matrace.factorNon-Hispanic Black -0.0221496  0.0241286  -0.918 0.358956    
## matrace.factorHispanic           -0.0576458  0.0292231  -1.973 0.048946 *  
## matrace.factorOther              -0.0767131  0.0487136  -1.575 0.115775    
## cm1inpov                         -0.0036416  0.0038964  -0.935 0.350326    
## m1b2                              0.0003721  0.0227086   0.016 0.986933    
## Epithelial.cells                 -0.6148671  0.1709608  -3.597 0.000346 ***
## Leukocytes                       -3.5844191  0.1609197 -22.275  < 2e-16 ***
## age1.pm10                         0.0023906  0.0042399   0.564 0.573057    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.216 on 677 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.8374, Adjusted R-squared:  0.8348 
## F-statistic:   317 on 11 and 677 DF,  p-value: < 2.2e-16
output$IQRcoef.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.c[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Centered & Scaled

mod<-lm(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm25, na.option=na.exclude,  data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm25.centscalestd ~ birth.pm25 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + age1.pm25, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.07142 -0.23587 -0.02596  0.20037  1.37240 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       4.7731008  0.2106251  22.662   <2e-16 ***
## birth.pm25                       -0.0005382  0.0015551  -0.346   0.7293    
## age.dnam                         -0.0095454  0.0030016  -3.180   0.0015 ** 
## sexm                              0.0368750  0.0184561   1.998   0.0459 *  
## matrace.factorNon-Hispanic Black  0.2708927  0.0272759   9.932   <2e-16 ***
## matrace.factorHispanic            0.0287007  0.0312594   0.918   0.3587    
## matrace.factorOther               0.0969381  0.0546411   1.774   0.0763 .  
## cm1inpov                          0.0069474  0.0043656   1.591   0.1117    
## m1b2                              0.0347672  0.0249161   1.395   0.1631    
## Epithelial.cells                 -2.9776961  0.1939245 -15.355   <2e-16 ***
## Leukocytes                       -4.7500121  0.1803100 -26.344   <2e-16 ***
## age1.pm25                        -0.0015921  0.0020849  -0.764   0.4452    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.349 on 1442 degrees of freedom
##   (88 observations deleted due to missingness)
## Multiple R-squared:  0.549,  Adjusted R-squared:  0.5455 
## F-statistic: 159.6 on 11 and 1442 DF,  p-value: < 2.2e-16
summary(mod)$coef[2,4]
## [1] 0.7293468
mod.a9<-lm(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm25, na.option=na.exclude,  data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm25.centscalestd ~ birth.pm25 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + age1.pm25, data = include[include$childteen == 
##     "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.90143 -0.22864 -0.03509  0.18074  1.38416 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       4.0401353  0.4816352   8.388 2.73e-16 ***
## birth.pm25                       -0.0002617  0.0022507  -0.116   0.9075    
## age.dnam                          0.0579659  0.0412936   1.404   0.1608    
## sexm                              0.0521633  0.0260114   2.005   0.0453 *  
## matrace.factorNon-Hispanic Black  0.2845253  0.0384886   7.392 4.14e-13 ***
## matrace.factorHispanic            0.0128858  0.0445665   0.289   0.7726    
## matrace.factorOther               0.0915052  0.0776638   1.178   0.2391    
## cm1inpov                          0.0103898  0.0061670   1.685   0.0925 .  
## m1b2                              0.0272370  0.0352506   0.773   0.4400    
## Epithelial.cells                 -2.7715383  0.2860236  -9.690  < 2e-16 ***
## Leukocytes                       -4.6560772  0.2625295 -17.735  < 2e-16 ***
## age1.pm25                        -0.0020489  0.0029273  -0.700   0.4842    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3437 on 698 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.5427, Adjusted R-squared:  0.5355 
## F-statistic: 75.31 on 11 and 698 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm25, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm25.centscalestd ~ birth.pm25 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + age1.pm25, data = include[include$childteen == 
##     "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.06953 -0.24448 -0.01992  0.19661  1.33303 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       4.1977732  0.5139527   8.168 1.38e-15 ***
## birth.pm25                        0.0004206  0.0022259   0.189    0.850    
## age.dnam                          0.0327570  0.0275081   1.191    0.234    
## sexm                              0.0220033  0.0263187   0.836    0.403    
## matrace.factorNon-Hispanic Black  0.2548602  0.0389853   6.537 1.17e-10 ***
## matrace.factorHispanic            0.0301029  0.0447731   0.672    0.502    
## matrace.factorOther               0.1100420  0.0771275   1.427    0.154    
## cm1inpov                          0.0056298  0.0062419   0.902    0.367    
## m1b2                              0.0473426  0.0353708   1.338    0.181    
## Epithelial.cells                 -3.1592838  0.2675486 -11.808  < 2e-16 ***
## Leukocytes                       -4.8557286  0.2511693 -19.332  < 2e-16 ***
## age1.pm25                        -0.0011846  0.0029784  -0.398    0.691    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3545 on 732 degrees of freedom
##   (49 observations deleted due to missingness)
## Multiple R-squared:  0.5599, Adjusted R-squared:  0.5533 
## F-statistic: 84.67 on 11 and 732 DF,  p-value: < 2.2e-16
output$IQRcoef.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.cs[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm10, na.option=na.exclude, data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm10.centscalestd ~ birth.pm10 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + age1.pm10, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.51678 -0.11799 -0.01851  0.09456  1.58212 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       0.843360   0.119095   7.081 2.30e-12 ***
## birth.pm10                       -0.006186   0.002448  -2.526   0.0116 *  
## age.dnam                          0.007383   0.001676   4.406 1.14e-05 ***
## sexm                              0.014233   0.010240   1.390   0.1648    
## matrace.factorNon-Hispanic Black -0.014526   0.014817  -0.980   0.3271    
## matrace.factorHispanic           -0.038088   0.017950  -2.122   0.0340 *  
## matrace.factorOther              -0.060204   0.030505  -1.974   0.0486 *  
## cm1inpov                         -0.003402   0.002391  -1.423   0.1550    
## m1b2                             -0.010331   0.014051  -0.735   0.4623    
## Epithelial.cells                 -0.174514   0.107935  -1.617   0.1062    
## Leukocytes                       -0.923087   0.100424  -9.192  < 2e-16 ***
## age1.pm10                         0.002663   0.002599   1.025   0.3057    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1859 on 1330 degrees of freedom
##   (200 observations deleted due to missingness)
## Multiple R-squared:    0.3,  Adjusted R-squared:  0.2942 
## F-statistic: 51.81 on 11 and 1330 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm10, na.option=na.exclude, data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm10.centscalestd ~ birth.pm10 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + age1.pm10, data = include[include$childteen == 
##     "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.50052 -0.11915 -0.01944  0.09313  1.45952 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       0.668155   0.290200   2.302   0.0216 *  
## birth.pm10                       -0.004247   0.003540  -1.200   0.2307    
## age.dnam                          0.006249   0.025858   0.242   0.8091    
## sexm                              0.013479   0.014926   0.903   0.3668    
## matrace.factorNon-Hispanic Black  0.012517   0.021610   0.579   0.5627    
## matrace.factorHispanic           -0.004720   0.026246  -0.180   0.8573    
## matrace.factorOther              -0.037103   0.045396  -0.817   0.4140    
## cm1inpov                         -0.002716   0.003491  -0.778   0.4368    
## m1b2                             -0.025358   0.020593  -1.231   0.2186    
## Epithelial.cells                 -0.038016   0.163319  -0.233   0.8160    
## Leukocytes                       -0.782883   0.149333  -5.243 2.15e-07 ***
## age1.pm10                         0.003332   0.003784   0.880   0.3789    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1892 on 641 degrees of freedom
##   (96 observations deleted due to missingness)
## Multiple R-squared:  0.2328, Adjusted R-squared:  0.2196 
## F-statistic: 17.68 on 11 and 641 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm10, na.option=na.exclude, data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm10.centscalestd ~ birth.pm10 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + age1.pm10, data = include[include$childteen == 
##     "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.46770 -0.11734 -0.01285  0.10117  1.60246 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       0.768462   0.308510   2.491  0.01298 *  
## birth.pm10                       -0.008316   0.003411  -2.438  0.01503 *  
## age.dnam                          0.024339   0.017703   1.375  0.16964    
## sexm                              0.014850   0.014157   1.049  0.29458    
## matrace.factorNon-Hispanic Black -0.041162   0.020467  -2.011  0.04471 *  
## matrace.factorHispanic           -0.072100   0.024789  -2.909  0.00375 ** 
## matrace.factorOther              -0.080095   0.041321  -1.938  0.05300 .  
## cm1inpov                         -0.003942   0.003305  -1.193  0.23335    
## m1b2                              0.002992   0.019263   0.155  0.87659    
## Epithelial.cells                 -0.295340   0.145018  -2.037  0.04208 *  
## Leukocytes                       -1.054264   0.136501  -7.724 4.08e-14 ***
## age1.pm10                         0.001698   0.003597   0.472  0.63701    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1832 on 677 degrees of freedom
##   (104 observations deleted due to missingness)
## Multiple R-squared:  0.3347, Adjusted R-squared:  0.3239 
## F-statistic: 30.96 on 11 and 677 DF,  p-value: < 2.2e-16
output$IQRcoef.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.cs[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Output

output
##   Exposure Age    N    IQRcoef.r    IQRlcl.r     IQRucl.r     pval.r
## 1    PM2.5 All 1454 -0.016189337 -0.05617041  0.023791737 0.42714851
## 2    PM2.5   9  710 -0.010120701 -0.06819248  0.047951074 0.73232278
## 3    PM2.5  15  744 -0.005841499 -0.06301159  0.051328591 0.84107022
## 4     PM10 All 1342 -0.022362783 -0.04051847 -0.004207094 0.01581171
## 5     PM10   9  653 -0.014315321 -0.04046339  0.011832746 0.28275492
## 6     PM10  15  689 -0.030180213 -0.05563138 -0.004729051 0.02018953
##      IQRcoef.c    IQRlcl.c     IQRucl.c     pval.c   IQRcoef.cs   IQRlcl.cs
## 1 -0.016189337 -0.05617041  0.023791737 0.42714851 -0.005781016 -0.03855003
## 2 -0.010120701 -0.06819248  0.047951074 0.73232278 -0.002811276 -0.05027987
## 3 -0.005841499 -0.06301159  0.051328591 0.84107022  0.004518122 -0.04242230
## 4 -0.022362783 -0.04051847 -0.004207094 0.01581171 -0.019939726 -0.03542339
## 5 -0.014315321 -0.04046339  0.011832746 0.28275492 -0.013690741 -0.03609845
## 6 -0.030180213 -0.05563138 -0.004729051 0.02018953 -0.026805827 -0.04839487
##      IQRucl.cs    pval.cs
## 1  0.026988001 0.72934685
## 2  0.044657318 0.90746538
## 3  0.051458545 0.85017389
## 4 -0.004456063 0.01164158
## 5  0.008716970 0.23067156
## 6 -0.005216781 0.01502792
write.csv(output, file=here("Output",paste0("FFCW_AirPoll_Std_PEG_Regression_Sens_Age1_", date, ".csv")) )
output<-data.frame(matrix(nrow=6, ncol= 15))
colnames(output) <- c("Exposure", "Age", "N", "IQRcoef.r", "IQRlcl.r", "IQRucl.r", "pval.r",  "IQRcoef.c", "IQRlcl.c", "IQRucl.c", "pval.c",  "IQRcoef.cs", "IQRlcl.cs", "IQRucl.cs", "pval.cs")
output$Exposure <- c(rep("PM2.5", 3), rep("PM10", 3))
output$Age <- c("All", "9", "15")

IQR(include$birth.pm25, na.rm=T)
## [1] 10.74194
IQR(include$birth.pm10, na.rm=T)
## [1] 3.223546
### Raw

mod<-lm(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm25, na.option=na.exclude, data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age3.pm25, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.42781 -0.30304 -0.03394  0.24447  1.60793 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       6.164240   0.262369  23.495  < 2e-16 ***
## birth.pm25                       -0.003190   0.002038  -1.565  0.11780    
## age.dnam                         -0.006828   0.003813  -1.791  0.07352 .  
## sexm                              0.068065   0.023429   2.905  0.00373 ** 
## matrace.factorNon-Hispanic Black  0.439514   0.035663  12.324  < 2e-16 ***
## matrace.factorHispanic            0.037948   0.040287   0.942  0.34639    
## matrace.factorOther               0.114772   0.067114   1.710  0.08747 .  
## cm1inpov                          0.007832   0.005545   1.412  0.15804    
## m1b2                              0.016962   0.031869   0.532  0.59465    
## Epithelial.cells                 -3.196061   0.243035 -13.151  < 2e-16 ***
## Leukocytes                       -6.247030   0.226190 -27.618  < 2e-16 ***
## age3.pm25                         0.001717   0.001879   0.914  0.36109    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4351 on 1393 degrees of freedom
##   (137 observations deleted due to missingness)
## Multiple R-squared:  0.6655, Adjusted R-squared:  0.6629 
## F-statistic:   252 on 11 and 1393 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm25, na.option=na.exclude,  data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age3.pm25, data = include[include$childteen == "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.96909 -0.29609 -0.03167  0.25482  1.37356 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.2297071  0.6099248   8.574  < 2e-16 ***
## birth.pm25                       -0.0015381  0.0029785  -0.516   0.6057    
## age.dnam                          0.0873122  0.0521142   1.675   0.0943 .  
## sexm                              0.0803807  0.0331566   2.424   0.0156 *  
## matrace.factorNon-Hispanic Black  0.4479802  0.0505202   8.867  < 2e-16 ***
## matrace.factorHispanic            0.0079680  0.0576371   0.138   0.8901    
## matrace.factorOther               0.1154361  0.0955654   1.208   0.2275    
## cm1inpov                          0.0122369  0.0078849   1.552   0.1211    
## m1b2                              0.0181029  0.0452845   0.400   0.6895    
## Epithelial.cells                 -2.9994094  0.3595145  -8.343 4.11e-16 ***
## Leukocytes                       -6.2154867  0.3292929 -18.875  < 2e-16 ***
## age3.pm25                         0.0001656  0.0026597   0.062   0.9504    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4294 on 672 degrees of freedom
##   (65 observations deleted due to missingness)
## Multiple R-squared:  0.6474, Adjusted R-squared:  0.6416 
## F-statistic: 112.2 on 11 and 672 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm25, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age3.pm25, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.41407 -0.29311 -0.03333  0.23867  1.57063 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.241821   0.635801   8.244 8.03e-16 ***
## birth.pm25                       -0.002495   0.002918  -0.855    0.393    
## age.dnam                          0.055216   0.034257   1.612    0.107    
## sexm                              0.053798   0.033204   1.620    0.106    
## matrace.factorNon-Hispanic Black  0.426425   0.050718   8.408 2.29e-16 ***
## matrace.factorHispanic            0.046981   0.057282   0.820    0.412    
## matrace.factorOther               0.124525   0.094470   1.318    0.188    
## cm1inpov                          0.006580   0.007888   0.834    0.405    
## m1b2                              0.027412   0.045081   0.608    0.543    
## Epithelial.cells                 -3.356160   0.334541 -10.032  < 2e-16 ***
## Leukocytes                       -6.312358   0.314007 -20.103  < 2e-16 ***
## age3.pm25                         0.002453   0.002691   0.912    0.362    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4407 on 709 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:  0.6833, Adjusted R-squared:  0.6784 
## F-statistic: 139.1 on 11 and 709 DF,  p-value: < 2.2e-16
output$N[1:3] <- c(nrow(model.frame(mod)), nrow(model.frame(mod.a9)), nrow(model.frame(mod.a15)))

output$IQRcoef.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.r[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm10, na.option=na.exclude,  data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age3.pm10, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63808 -0.14440 -0.01354  0.12343  1.17331 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.5223007  0.1404187  25.084  < 2e-16 ***
## birth.pm10                       -0.0055738  0.0029378  -1.897   0.0580 .  
## age.dnam                          0.0055365  0.0020116   2.752   0.0060 ** 
## sexm                              0.0314583  0.0123116   2.555   0.0107 *  
## matrace.factorNon-Hispanic Black -0.0036982  0.0179286  -0.206   0.8366    
## matrace.factorHispanic           -0.0343640  0.0217333  -1.581   0.1141    
## matrace.factorOther              -0.0677908  0.0355550  -1.907   0.0568 .  
## cm1inpov                         -0.0026101  0.0028866  -0.904   0.3660    
## m1b2                             -0.0128951  0.0169647  -0.760   0.4473    
## Epithelial.cells                 -0.6426241  0.1283507  -5.007  6.3e-07 ***
## Leukocytes                       -3.5440795  0.1193826 -29.687  < 2e-16 ***
## age3.pm10                         0.0006073  0.0027401   0.222   0.8246    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.22 on 1297 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.8176, Adjusted R-squared:  0.816 
## F-statistic: 528.4 on 11 and 1297 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm10, na.option=na.exclude,  data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age3.pm10, data = include[include$childteen == "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.59862 -0.15550 -0.01583  0.12399  1.19183 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.0984444  0.3414566   9.074  < 2e-16 ***
## birth.pm10                       -0.0011074  0.0042188  -0.262 0.793027    
## age.dnam                          0.0427722  0.0304523   1.405 0.160650    
## sexm                              0.0183366  0.0177737   1.032 0.302626    
## matrace.factorNon-Hispanic Black  0.0195534  0.0259802   0.753 0.451959    
## matrace.factorHispanic           -0.0160700  0.0315094  -0.510 0.610226    
## matrace.factorOther              -0.0478593  0.0522753  -0.916 0.360271    
## cm1inpov                         -0.0007079  0.0041830  -0.169 0.865662    
## m1b2                             -0.0211416  0.0246531  -0.858 0.391464    
## Epithelial.cells                 -0.6373803  0.1925243  -3.311 0.000985 ***
## Leukocytes                       -3.5036984  0.1753347 -19.983  < 2e-16 ***
## age3.pm10                        -0.0013604  0.0040043  -0.340 0.734166    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2215 on 623 degrees of freedom
##   (114 observations deleted due to missingness)
## Multiple R-squared:  0.7728, Adjusted R-squared:  0.7687 
## F-statistic: 192.6 on 11 and 623 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm10, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age3.pm10, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5978 -0.1444 -0.0099  0.1274  1.1804 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.089361   0.369619   8.358 3.75e-16 ***
## birth.pm10                       -0.010652   0.004122  -2.584  0.00998 ** 
## age.dnam                          0.041063   0.021488   1.911  0.05644 .  
## sexm                              0.042399   0.017146   2.473  0.01366 *  
## matrace.factorNon-Hispanic Black -0.027266   0.024912  -1.094  0.27414    
## matrace.factorHispanic           -0.056172   0.030174  -1.862  0.06310 .  
## matrace.factorOther              -0.081835   0.048631  -1.683  0.09289 .  
## cm1inpov                         -0.003671   0.004013  -0.915  0.36058    
## m1b2                             -0.005638   0.023420  -0.241  0.80982    
## Epithelial.cells                 -0.683140   0.174193  -3.922 9.71e-05 ***
## Leukocytes                       -3.608341   0.163930 -22.012  < 2e-16 ***
## age3.pm10                         0.002886   0.003792   0.761  0.44691    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2188 on 662 degrees of freedom
##   (119 observations deleted due to missingness)
## Multiple R-squared:  0.8441, Adjusted R-squared:  0.8415 
## F-statistic: 325.8 on 11 and 662 DF,  p-value: < 2.2e-16
output$N[4:6] <- c(nrow(model.frame(mod)), nrow(model.frame(mod.a9)), nrow(model.frame(mod.a15)))

output$IQRcoef.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.r[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Centered

mod<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm25, na.option=na.exclude,  data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age3.pm25, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.42781 -0.30304 -0.03394  0.24447  1.60793 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       6.164240   0.262369  23.495  < 2e-16 ***
## birth.pm25                       -0.003190   0.002038  -1.565  0.11780    
## age.dnam                         -0.006828   0.003813  -1.791  0.07352 .  
## sexm                              0.068065   0.023429   2.905  0.00373 ** 
## matrace.factorNon-Hispanic Black  0.439514   0.035663  12.324  < 2e-16 ***
## matrace.factorHispanic            0.037948   0.040287   0.942  0.34639    
## matrace.factorOther               0.114772   0.067114   1.710  0.08747 .  
## cm1inpov                          0.007832   0.005545   1.412  0.15804    
## m1b2                              0.016962   0.031869   0.532  0.59465    
## Epithelial.cells                 -3.196061   0.243035 -13.151  < 2e-16 ***
## Leukocytes                       -6.247030   0.226190 -27.618  < 2e-16 ***
## age3.pm25                         0.001717   0.001879   0.914  0.36109    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4351 on 1393 degrees of freedom
##   (137 observations deleted due to missingness)
## Multiple R-squared:  0.6655, Adjusted R-squared:  0.6629 
## F-statistic:   252 on 11 and 1393 DF,  p-value: < 2.2e-16
summary(mod)$coef[2,4]
## [1] 0.1178041
mod.a9<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm25, na.option=na.exclude,  data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age3.pm25, data = include[include$childteen == "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.96909 -0.29609 -0.03167  0.25482  1.37356 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.2297071  0.6099248   8.574  < 2e-16 ***
## birth.pm25                       -0.0015381  0.0029785  -0.516   0.6057    
## age.dnam                          0.0873122  0.0521142   1.675   0.0943 .  
## sexm                              0.0803807  0.0331566   2.424   0.0156 *  
## matrace.factorNon-Hispanic Black  0.4479802  0.0505202   8.867  < 2e-16 ***
## matrace.factorHispanic            0.0079680  0.0576371   0.138   0.8901    
## matrace.factorOther               0.1154361  0.0955654   1.208   0.2275    
## cm1inpov                          0.0122369  0.0078849   1.552   0.1211    
## m1b2                              0.0181029  0.0452845   0.400   0.6895    
## Epithelial.cells                 -2.9994094  0.3595145  -8.343 4.11e-16 ***
## Leukocytes                       -6.2154867  0.3292929 -18.875  < 2e-16 ***
## age3.pm25                         0.0001656  0.0026597   0.062   0.9504    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4294 on 672 degrees of freedom
##   (65 observations deleted due to missingness)
## Multiple R-squared:  0.6474, Adjusted R-squared:  0.6416 
## F-statistic: 112.2 on 11 and 672 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm25, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age3.pm25, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.41407 -0.29311 -0.03333  0.23867  1.57063 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.241821   0.635801   8.244 8.03e-16 ***
## birth.pm25                       -0.002495   0.002918  -0.855    0.393    
## age.dnam                          0.055216   0.034257   1.612    0.107    
## sexm                              0.053798   0.033204   1.620    0.106    
## matrace.factorNon-Hispanic Black  0.426425   0.050718   8.408 2.29e-16 ***
## matrace.factorHispanic            0.046981   0.057282   0.820    0.412    
## matrace.factorOther               0.124525   0.094470   1.318    0.188    
## cm1inpov                          0.006580   0.007888   0.834    0.405    
## m1b2                              0.027412   0.045081   0.608    0.543    
## Epithelial.cells                 -3.356160   0.334541 -10.032  < 2e-16 ***
## Leukocytes                       -6.312358   0.314007 -20.103  < 2e-16 ***
## age3.pm25                         0.002453   0.002691   0.912    0.362    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4407 on 709 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:  0.6833, Adjusted R-squared:  0.6784 
## F-statistic: 139.1 on 11 and 709 DF,  p-value: < 2.2e-16
output$IQRcoef.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.c[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm10, na.option=na.exclude, data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age3.pm10, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63808 -0.14440 -0.01354  0.12343  1.17331 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.5223007  0.1404187  25.084  < 2e-16 ***
## birth.pm10                       -0.0055738  0.0029378  -1.897   0.0580 .  
## age.dnam                          0.0055365  0.0020116   2.752   0.0060 ** 
## sexm                              0.0314583  0.0123116   2.555   0.0107 *  
## matrace.factorNon-Hispanic Black -0.0036982  0.0179286  -0.206   0.8366    
## matrace.factorHispanic           -0.0343640  0.0217333  -1.581   0.1141    
## matrace.factorOther              -0.0677908  0.0355550  -1.907   0.0568 .  
## cm1inpov                         -0.0026101  0.0028866  -0.904   0.3660    
## m1b2                             -0.0128951  0.0169647  -0.760   0.4473    
## Epithelial.cells                 -0.6426241  0.1283507  -5.007  6.3e-07 ***
## Leukocytes                       -3.5440795  0.1193826 -29.687  < 2e-16 ***
## age3.pm10                         0.0006073  0.0027401   0.222   0.8246    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.22 on 1297 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.8176, Adjusted R-squared:  0.816 
## F-statistic: 528.4 on 11 and 1297 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm10, na.option=na.exclude, data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age3.pm10, data = include[include$childteen == "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.59862 -0.15550 -0.01583  0.12399  1.19183 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.0984444  0.3414566   9.074  < 2e-16 ***
## birth.pm10                       -0.0011074  0.0042188  -0.262 0.793027    
## age.dnam                          0.0427722  0.0304523   1.405 0.160650    
## sexm                              0.0183366  0.0177737   1.032 0.302626    
## matrace.factorNon-Hispanic Black  0.0195534  0.0259802   0.753 0.451959    
## matrace.factorHispanic           -0.0160700  0.0315094  -0.510 0.610226    
## matrace.factorOther              -0.0478593  0.0522753  -0.916 0.360271    
## cm1inpov                         -0.0007079  0.0041830  -0.169 0.865662    
## m1b2                             -0.0211416  0.0246531  -0.858 0.391464    
## Epithelial.cells                 -0.6373803  0.1925243  -3.311 0.000985 ***
## Leukocytes                       -3.5036984  0.1753347 -19.983  < 2e-16 ***
## age3.pm10                        -0.0013604  0.0040043  -0.340 0.734166    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2215 on 623 degrees of freedom
##   (114 observations deleted due to missingness)
## Multiple R-squared:  0.7728, Adjusted R-squared:  0.7687 
## F-statistic: 192.6 on 11 and 623 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm10, na.option=na.exclude, data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     age3.pm10, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5978 -0.1444 -0.0099  0.1274  1.1804 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.089361   0.369619   8.358 3.75e-16 ***
## birth.pm10                       -0.010652   0.004122  -2.584  0.00998 ** 
## age.dnam                          0.041063   0.021488   1.911  0.05644 .  
## sexm                              0.042399   0.017146   2.473  0.01366 *  
## matrace.factorNon-Hispanic Black -0.027266   0.024912  -1.094  0.27414    
## matrace.factorHispanic           -0.056172   0.030174  -1.862  0.06310 .  
## matrace.factorOther              -0.081835   0.048631  -1.683  0.09289 .  
## cm1inpov                         -0.003671   0.004013  -0.915  0.36058    
## m1b2                             -0.005638   0.023420  -0.241  0.80982    
## Epithelial.cells                 -0.683140   0.174193  -3.922 9.71e-05 ***
## Leukocytes                       -3.608341   0.163930 -22.012  < 2e-16 ***
## age3.pm10                         0.002886   0.003792   0.761  0.44691    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2188 on 662 degrees of freedom
##   (119 observations deleted due to missingness)
## Multiple R-squared:  0.8441, Adjusted R-squared:  0.8415 
## F-statistic: 325.8 on 11 and 662 DF,  p-value: < 2.2e-16
output$IQRcoef.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.c[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Centered & Scaled

mod<-lm(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm25, na.option=na.exclude,  data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm25.centscalestd ~ birth.pm25 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + age3.pm25, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.04097 -0.24889 -0.02739  0.20479  1.40252 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       4.7840483  0.2147519  22.277  < 2e-16 ***
## birth.pm25                       -0.0020194  0.0016684  -1.210  0.22633    
## age.dnam                         -0.0090969  0.0031208  -2.915  0.00361 ** 
## sexm                              0.0532416  0.0191772   2.776  0.00557 ** 
## matrace.factorNon-Hispanic Black  0.2674298  0.0291902   9.162  < 2e-16 ***
## matrace.factorHispanic            0.0342985  0.0329750   1.040  0.29846    
## matrace.factorOther               0.0631528  0.0549333   1.150  0.25049    
## cm1inpov                          0.0077335  0.0045388   1.704  0.08863 .  
## m1b2                              0.0112634  0.0260852   0.432  0.66596    
## Epithelial.cells                 -2.9926796  0.1989265 -15.044  < 2e-16 ***
## Leukocytes                       -4.7441477  0.1851391 -25.625  < 2e-16 ***
## age3.pm25                         0.0004215  0.0015380   0.274  0.78406    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3561 on 1393 degrees of freedom
##   (137 observations deleted due to missingness)
## Multiple R-squared:  0.5451, Adjusted R-squared:  0.5415 
## F-statistic: 151.7 on 11 and 1393 DF,  p-value: < 2.2e-16
summary(mod)$coef[2,4]
## [1] 0.2263328
mod.a9<-lm(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm25, na.option=na.exclude,  data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm25.centscalestd ~ birth.pm25 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + age3.pm25, data = include[include$childteen == 
##     "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.87294 -0.24427 -0.02619  0.19119  1.41898 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.9227365  0.4999197   7.847 1.69e-14 ***
## birth.pm25                       -0.0007144  0.0024413  -0.293  0.76990    
## age.dnam                          0.0654955  0.0427149   1.533  0.12567    
## sexm                              0.0740241  0.0271765   2.724  0.00662 ** 
## matrace.factorNon-Hispanic Black  0.2764441  0.0414085   6.676 5.14e-11 ***
## matrace.factorHispanic            0.0184707  0.0472418   0.391  0.69593    
## matrace.factorOther               0.0539007  0.0783293   0.688  0.49161    
## cm1inpov                          0.0115888  0.0064628   1.793  0.07340 .  
## m1b2                              0.0068607  0.0371170   0.185  0.85341    
## Epithelial.cells                 -2.7151331  0.2946730  -9.214  < 2e-16 ***
## Leukocytes                       -4.6089421  0.2699021 -17.076  < 2e-16 ***
## age3.pm25                        -0.0006915  0.0021800  -0.317  0.75119    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.352 on 672 degrees of freedom
##   (65 observations deleted due to missingness)
## Multiple R-squared:  0.5298, Adjusted R-squared:  0.5221 
## F-statistic: 68.84 on 11 and 672 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm25, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm25.centscalestd ~ birth.pm25 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + age3.pm25, data = include[include$childteen == 
##     "T", ], na.option = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0496 -0.2527 -0.0213  0.1972  1.3141 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       4.1578690  0.5193712   8.006 4.87e-15 ***
## birth.pm25                       -0.0013802  0.0023839  -0.579    0.563    
## age.dnam                          0.0400184  0.0279836   1.430    0.153    
## sexm                              0.0318395  0.0271235   1.174    0.241    
## matrace.factorNon-Hispanic Black  0.2555726  0.0414304   6.169 1.16e-09 ***
## matrace.factorHispanic            0.0331699  0.0467922   0.709    0.479    
## matrace.factorOther               0.0801459  0.0771708   1.039    0.299    
## cm1inpov                          0.0062623  0.0064439   0.972    0.331    
## m1b2                              0.0243333  0.0368257   0.661    0.509    
## Epithelial.cells                 -3.2221092  0.2732787 -11.791  < 2e-16 ***
## Leukocytes                       -4.8910617  0.2565054 -19.068  < 2e-16 ***
## age3.pm25                         0.0008211  0.0021979   0.374    0.709    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.36 on 709 degrees of freedom
##   (72 observations deleted due to missingness)
## Multiple R-squared:  0.5649, Adjusted R-squared:  0.5582 
## F-statistic: 83.68 on 11 and 709 DF,  p-value: < 2.2e-16
output$IQRcoef.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.cs[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm10, na.option=na.exclude, data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm10.centscalestd ~ birth.pm10 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + age3.pm10, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.51719 -0.11953 -0.02084  0.09561  1.58381 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       0.8358744  0.1198916   6.972 4.97e-12 ***
## birth.pm10                       -0.0049344  0.0025084  -1.967   0.0494 *  
## age.dnam                          0.0075196  0.0017176   4.378 1.29e-05 ***
## sexm                              0.0164823  0.0105119   1.568   0.1171    
## matrace.factorNon-Hispanic Black -0.0174400  0.0153077  -1.139   0.2548    
## matrace.factorHispanic           -0.0409818  0.0185562  -2.209   0.0274 *  
## matrace.factorOther              -0.0662788  0.0303574  -2.183   0.0292 *  
## cm1inpov                         -0.0029165  0.0024646  -1.183   0.2369    
## m1b2                             -0.0127223  0.0144847  -0.878   0.3799    
## Epithelial.cells                 -0.1520786  0.1095878  -1.388   0.1655    
## Leukocytes                       -0.9024079  0.1019307  -8.853  < 2e-16 ***
## age3.pm10                         0.0005452  0.0023396   0.233   0.8158    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1878 on 1297 degrees of freedom
##   (233 observations deleted due to missingness)
## Multiple R-squared:  0.3084, Adjusted R-squared:  0.3025 
## F-statistic: 52.58 on 11 and 1297 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm10, na.option=na.exclude, data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm10.centscalestd ~ birth.pm10 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + age3.pm10, data = include[include$childteen == 
##     "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.49715 -0.11942 -0.02170  0.09196  1.46566 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       0.650680   0.294608   2.209   0.0276 *  
## birth.pm10                       -0.002581   0.003640  -0.709   0.4786    
## age.dnam                          0.002256   0.026274   0.086   0.9316    
## sexm                              0.012600   0.015335   0.822   0.4116    
## matrace.factorNon-Hispanic Black  0.009871   0.022416   0.440   0.6598    
## matrace.factorHispanic           -0.009183   0.027186  -0.338   0.7357    
## matrace.factorOther              -0.044192   0.045103  -0.980   0.3276    
## cm1inpov                         -0.001512   0.003609  -0.419   0.6754    
## m1b2                             -0.026641   0.021271  -1.252   0.2109    
## Epithelial.cells                  0.023911   0.166109   0.144   0.8856    
## Leukocytes                       -0.721428   0.151278  -4.769 2.31e-06 ***
## age3.pm10                         0.001002   0.003455   0.290   0.7719    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1911 on 623 degrees of freedom
##   (114 observations deleted due to missingness)
## Multiple R-squared:  0.2264, Adjusted R-squared:  0.2127 
## F-statistic: 16.57 on 11 and 623 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm10, na.option=na.exclude, data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm10.centscalestd ~ birth.pm10 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + age3.pm10, data = include[include$childteen == 
##     "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.46763 -0.11800 -0.01315  0.10458  1.60316 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       0.7198509  0.3122599   2.305  0.02146 *  
## birth.pm10                       -0.0077267  0.0034825  -2.219  0.02685 *  
## age.dnam                          0.0305987  0.0181533   1.686  0.09235 .  
## sexm                              0.0195273  0.0144853   1.348  0.17810    
## matrace.factorNon-Hispanic Black -0.0440297  0.0210460  -2.092  0.03681 *  
## matrace.factorHispanic           -0.0732879  0.0254916  -2.875  0.00417 ** 
## matrace.factorOther              -0.0850695  0.0410844  -2.071  0.03878 *  
## cm1inpov                         -0.0041726  0.0033902  -1.231  0.21884    
## m1b2                             -0.0006218  0.0197856  -0.031  0.97494    
## Epithelial.cells                 -0.3182779  0.1471616  -2.163  0.03092 *  
## Leukocytes                       -1.0752164  0.1384906  -7.764 3.14e-14 ***
## age3.pm10                        -0.0001602  0.0032039  -0.050  0.96013    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1849 on 662 degrees of freedom
##   (119 observations deleted due to missingness)
## Multiple R-squared:  0.3542, Adjusted R-squared:  0.3434 
## F-statistic:    33 on 11 and 662 DF,  p-value: < 2.2e-16
output$IQRcoef.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.cs[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Output

output
##   Exposure Age    N    IQRcoef.r    IQRlcl.r      IQRucl.r      pval.r
## 1    PM2.5 All 1405 -0.034267487 -0.07721979  0.0086848164 0.117804073
## 2    PM2.5   9  684 -0.016522589 -0.07934382  0.0462986371 0.605731296
## 3    PM2.5  15  721 -0.026799538 -0.08834585  0.0347467777 0.392895258
## 4     PM10 All 1309 -0.017967483 -0.03654604  0.0006110712 0.058014393
## 5     PM10   9  635 -0.003569828 -0.03027647  0.0231368113 0.793026788
## 6     PM10  15  674 -0.034338258 -0.06043038 -0.0082461401 0.009975976
##      IQRcoef.c    IQRlcl.c      IQRucl.c      pval.c   IQRcoef.cs   IQRlcl.cs
## 1 -0.034267487 -0.07721979  0.0086848164 0.117804073 -0.021692625 -0.05684954
## 2 -0.016522589 -0.07934382  0.0462986371 0.605731296 -0.007673835 -0.05916471
## 3 -0.026799538 -0.08834585  0.0347467777 0.392895258 -0.014826456 -0.06510226
## 4 -0.017967483 -0.03654604  0.0006110712 0.058014393 -0.015906123 -0.03176878
## 5 -0.003569828 -0.03027647  0.0231368113 0.793026788 -0.008318897 -0.03136131
## 6 -0.034338258 -0.06043038 -0.0082461401 0.009975976 -0.024907312 -0.04695037
##       IQRucl.cs    pval.cs
## 1  1.346429e-02 0.22633284
## 2  4.381704e-02 0.76989826
## 3  3.544935e-02 0.56278216
## 4 -4.347107e-05 0.04937638
## 5  1.472352e-02 0.47860573
## 6 -2.864251e-03 0.02684583
write.csv(output, file=here("Output",paste0("FFCW_AirPoll_Std_PEG_Regression_Sens_Age3_", date, ".csv")) )
output<-data.frame(matrix(nrow=6, ncol= 15))
colnames(output) <- c("Exposure", "Age", "N", "IQRcoef.r", "IQRlcl.r", "IQRucl.r", "pval.r",  "IQRcoef.c", "IQRlcl.c", "IQRucl.c", "pval.c",  "IQRcoef.cs", "IQRlcl.cs", "IQRucl.cs", "pval.cs")
output$Exposure <- c(rep("PM2.5", 3), rep("PM10", 3))
output$Age <- c("All", "9", "15")

IQR(include$birth.pm25, na.rm=T)
## [1] 10.74194
IQR(include$birth.pm10, na.rm=T)
## [1] 3.223546
### Raw

mod<-lm(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm10, na.option=na.exclude, data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     birth.pm10, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.43798 -0.28813 -0.03526  0.23924  1.62276 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.958295   0.265844  22.413  < 2e-16 ***
## birth.pm25                       -0.002145   0.001866  -1.149  0.25073    
## age.dnam                         -0.007930   0.003774  -2.101  0.03582 *  
## sexm                              0.048072   0.023021   2.088  0.03696 *  
## matrace.factorNon-Hispanic Black  0.435082   0.033717  12.904  < 2e-16 ***
## matrace.factorHispanic            0.053910   0.040918   1.318  0.18788    
## matrace.factorOther               0.141052   0.067852   2.079  0.03781 *  
## cm1inpov                          0.010402   0.005443   1.911  0.05622 .  
## m1b2                              0.056124   0.031761   1.767  0.07743 .  
## Epithelial.cells                 -3.187917   0.240373 -13.262  < 2e-16 ***
## Leukocytes                       -6.252160   0.224308 -27.873  < 2e-16 ***
## birth.pm10                        0.011265   0.004277   2.634  0.00854 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4308 on 1413 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:   0.67,  Adjusted R-squared:  0.6674 
## F-statistic: 260.8 on 11 and 1413 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm10, na.option=na.exclude,  data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     birth.pm10, data = include[include$childteen == "C", ], na.option = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9945 -0.2940 -0.0334  0.2495  1.3625 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.368196   0.619423   8.666   <2e-16 ***
## birth.pm25                       -0.002093   0.002638  -0.793   0.4280    
## age.dnam                          0.066072   0.054103   1.221   0.2224    
## sexm                              0.052085   0.032564   1.599   0.1102    
## matrace.factorNon-Hispanic Black  0.447850   0.047738   9.381   <2e-16 ***
## matrace.factorHispanic            0.014962   0.057812   0.259   0.7959    
## matrace.factorOther               0.137486   0.097379   1.412   0.1584    
## cm1inpov                          0.014049   0.007722   1.819   0.0693 .  
## m1b2                              0.049926   0.044989   1.110   0.2675    
## Epithelial.cells                 -3.120963   0.349966  -8.918   <2e-16 ***
## Leukocytes                       -6.297439   0.322337 -19.537   <2e-16 ***
## birth.pm10                        0.007774   0.005980   1.300   0.1940    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4243 on 678 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.6561, Adjusted R-squared:  0.6505 
## F-statistic: 117.6 on 11 and 678 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm10, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     birth.pm10, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.42607 -0.28494 -0.03792  0.22830  1.61025 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.765525   0.714423   8.070 2.92e-15 ***
## birth.pm25                       -0.002097   0.002707  -0.775   0.4387    
## age.dnam                         -0.001872   0.041691  -0.045   0.9642    
## sexm                              0.044220   0.032777   1.349   0.1777    
## matrace.factorNon-Hispanic Black  0.427654   0.047964   8.916  < 2e-16 ***
## matrace.factorHispanic            0.090606   0.058349   1.553   0.1209    
## matrace.factorOther               0.149050   0.095186   1.566   0.1178    
## cm1inpov                          0.007994   0.007750   1.032   0.3026    
## m1b2                              0.063542   0.045029   1.411   0.1586    
## Epithelial.cells                 -3.221414   0.335231  -9.610  < 2e-16 ***
## Leukocytes                       -6.218660   0.315287 -19.724  < 2e-16 ***
## birth.pm10                        0.015201   0.006185   2.458   0.0142 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4386 on 723 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.6825, Adjusted R-squared:  0.6777 
## F-statistic: 141.3 on 11 and 723 DF,  p-value: < 2.2e-16
output$N[1:3] <- c(nrow(model.frame(mod)), nrow(model.frame(mod.a9)), nrow(model.frame(mod.a15)))

output$IQRcoef.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.r[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm25, na.option=na.exclude,  data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     birth.pm25, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.65759 -0.14242 -0.01167  0.12246  1.16835 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.5234786  0.1343159  26.233  < 2e-16 ***
## birth.pm10                       -0.0041626  0.0021610  -1.926  0.05427 .  
## age.dnam                          0.0052850  0.0019069   2.771  0.00565 ** 
## sexm                              0.0298702  0.0116310   2.568  0.01033 *  
## matrace.factorNon-Hispanic Black  0.0035391  0.0170353   0.208  0.83545    
## matrace.factorHispanic           -0.0276025  0.0206734  -1.335  0.18204    
## matrace.factorOther              -0.0683293  0.0342815  -1.993  0.04643 *  
## cm1inpov                         -0.0023039  0.0027503  -0.838  0.40233    
## m1b2                             -0.0119113  0.0160470  -0.742  0.45804    
## Epithelial.cells                 -0.6361516  0.1214467  -5.238 1.87e-07 ***
## Leukocytes                       -3.5580208  0.1133303 -31.395  < 2e-16 ***
## birth.pm25                       -0.0001054  0.0009430  -0.112  0.91100    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2177 on 1413 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.8204, Adjusted R-squared:  0.819 
## F-statistic: 586.7 on 11 and 1413 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm25, na.option=na.exclude,  data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     birth.pm25, data = include[include$childteen == "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63355 -0.15101 -0.01642  0.12312  1.17879 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.1993328  0.3207631   9.974  < 2e-16 ***
## birth.pm10                       -0.0013328  0.0030968  -0.430 0.667053    
## age.dnam                          0.0309142  0.0280171   1.103 0.270242    
## sexm                              0.0207108  0.0168631   1.228 0.219810    
## matrace.factorNon-Hispanic Black  0.0222729  0.0247208   0.901 0.367922    
## matrace.factorHispanic           -0.0062221  0.0299374  -0.208 0.835419    
## matrace.factorOther              -0.0524898  0.0504267  -1.041 0.298288    
## cm1inpov                         -0.0013138  0.0039986  -0.329 0.742577    
## m1b2                             -0.0290504  0.0232972  -1.247 0.212847    
## Epithelial.cells                 -0.6103030  0.1812272  -3.368 0.000801 ***
## Leukocytes                       -3.5112384  0.1669194 -21.036  < 2e-16 ***
## birth.pm25                        0.0003696  0.0013662   0.271 0.786840    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2197 on 678 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.782,  Adjusted R-squared:  0.7785 
## F-statistic: 221.1 on 11 and 678 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm25, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     birth.pm25, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.60210 -0.13868 -0.00839  0.12768  1.18987 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.1906659  0.3522411   9.058  < 2e-16 ***
## birth.pm10                       -0.0071641  0.0030496  -2.349   0.0191 *  
## age.dnam                          0.0345156  0.0205553   1.679   0.0936 .  
## sexm                              0.0368913  0.0161603   2.283   0.0227 *  
## matrace.factorNon-Hispanic Black -0.0148861  0.0236483  -0.629   0.5292    
## matrace.factorHispanic           -0.0504615  0.0287684  -1.754   0.0798 .  
## matrace.factorOther              -0.0790522  0.0469308  -1.684   0.0925 .  
## cm1inpov                         -0.0026756  0.0038210  -0.700   0.4840    
## m1b2                              0.0044819  0.0222010   0.202   0.8401    
## Epithelial.cells                 -0.6877474  0.1652835  -4.161 3.55e-05 ***
## Leukocytes                       -3.6235009  0.1554501 -23.310  < 2e-16 ***
## birth.pm25                       -0.0007376  0.0013347  -0.553   0.5807    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2162 on 723 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.8438, Adjusted R-squared:  0.8415 
## F-statistic: 355.1 on 11 and 723 DF,  p-value: < 2.2e-16
output$N[4:6] <- c(nrow(model.frame(mod)), nrow(model.frame(mod.a9)), nrow(model.frame(mod.a15)))

output$IQRcoef.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.r[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Centered

mod<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm10, na.option=na.exclude,  data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     birth.pm10, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.43798 -0.28813 -0.03526  0.23924  1.62276 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.958295   0.265844  22.413  < 2e-16 ***
## birth.pm25                       -0.002145   0.001866  -1.149  0.25073    
## age.dnam                         -0.007930   0.003774  -2.101  0.03582 *  
## sexm                              0.048072   0.023021   2.088  0.03696 *  
## matrace.factorNon-Hispanic Black  0.435082   0.033717  12.904  < 2e-16 ***
## matrace.factorHispanic            0.053910   0.040918   1.318  0.18788    
## matrace.factorOther               0.141052   0.067852   2.079  0.03781 *  
## cm1inpov                          0.010402   0.005443   1.911  0.05622 .  
## m1b2                              0.056124   0.031761   1.767  0.07743 .  
## Epithelial.cells                 -3.187917   0.240373 -13.262  < 2e-16 ***
## Leukocytes                       -6.252160   0.224308 -27.873  < 2e-16 ***
## birth.pm10                        0.011265   0.004277   2.634  0.00854 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4308 on 1413 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:   0.67,  Adjusted R-squared:  0.6674 
## F-statistic: 260.8 on 11 and 1413 DF,  p-value: < 2.2e-16
summary(mod)$coef[2,4]
## [1] 0.2507349
mod.a9<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm10, na.option=na.exclude,  data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     birth.pm10, data = include[include$childteen == "C", ], na.option = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9945 -0.2940 -0.0334  0.2495  1.3625 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.368196   0.619423   8.666   <2e-16 ***
## birth.pm25                       -0.002093   0.002638  -0.793   0.4280    
## age.dnam                          0.066072   0.054103   1.221   0.2224    
## sexm                              0.052085   0.032564   1.599   0.1102    
## matrace.factorNon-Hispanic Black  0.447850   0.047738   9.381   <2e-16 ***
## matrace.factorHispanic            0.014962   0.057812   0.259   0.7959    
## matrace.factorOther               0.137486   0.097379   1.412   0.1584    
## cm1inpov                          0.014049   0.007722   1.819   0.0693 .  
## m1b2                              0.049926   0.044989   1.110   0.2675    
## Epithelial.cells                 -3.120963   0.349966  -8.918   <2e-16 ***
## Leukocytes                       -6.297439   0.322337 -19.537   <2e-16 ***
## birth.pm10                        0.007774   0.005980   1.300   0.1940    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4243 on 678 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.6561, Adjusted R-squared:  0.6505 
## F-statistic: 117.6 on 11 and 678 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm10, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     birth.pm10, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.42607 -0.28494 -0.03792  0.22830  1.61025 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       5.765525   0.714423   8.070 2.92e-15 ***
## birth.pm25                       -0.002097   0.002707  -0.775   0.4387    
## age.dnam                         -0.001872   0.041691  -0.045   0.9642    
## sexm                              0.044220   0.032777   1.349   0.1777    
## matrace.factorNon-Hispanic Black  0.427654   0.047964   8.916  < 2e-16 ***
## matrace.factorHispanic            0.090606   0.058349   1.553   0.1209    
## matrace.factorOther               0.149050   0.095186   1.566   0.1178    
## cm1inpov                          0.007994   0.007750   1.032   0.3026    
## m1b2                              0.063542   0.045029   1.411   0.1586    
## Epithelial.cells                 -3.221414   0.335231  -9.610  < 2e-16 ***
## Leukocytes                       -6.218660   0.315287 -19.724  < 2e-16 ***
## birth.pm10                        0.015201   0.006185   2.458   0.0142 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4386 on 723 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.6825, Adjusted R-squared:  0.6777 
## F-statistic: 141.3 on 11 and 723 DF,  p-value: < 2.2e-16
output$IQRcoef.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.c[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm25, na.option=na.exclude, data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     birth.pm25, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.65759 -0.14242 -0.01167  0.12246  1.16835 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.5234786  0.1343159  26.233  < 2e-16 ***
## birth.pm10                       -0.0041626  0.0021610  -1.926  0.05427 .  
## age.dnam                          0.0052850  0.0019069   2.771  0.00565 ** 
## sexm                              0.0298702  0.0116310   2.568  0.01033 *  
## matrace.factorNon-Hispanic Black  0.0035391  0.0170353   0.208  0.83545    
## matrace.factorHispanic           -0.0276025  0.0206734  -1.335  0.18204    
## matrace.factorOther              -0.0683293  0.0342815  -1.993  0.04643 *  
## cm1inpov                         -0.0023039  0.0027503  -0.838  0.40233    
## m1b2                             -0.0119113  0.0160470  -0.742  0.45804    
## Epithelial.cells                 -0.6361516  0.1214467  -5.238 1.87e-07 ***
## Leukocytes                       -3.5580208  0.1133303 -31.395  < 2e-16 ***
## birth.pm25                       -0.0001054  0.0009430  -0.112  0.91100    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2177 on 1413 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.8204, Adjusted R-squared:  0.819 
## F-statistic: 586.7 on 11 and 1413 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm25, na.option=na.exclude, data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     birth.pm25, data = include[include$childteen == "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.63355 -0.15101 -0.01642  0.12312  1.17879 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.1993328  0.3207631   9.974  < 2e-16 ***
## birth.pm10                       -0.0013328  0.0030968  -0.430 0.667053    
## age.dnam                          0.0309142  0.0280171   1.103 0.270242    
## sexm                              0.0207108  0.0168631   1.228 0.219810    
## matrace.factorNon-Hispanic Black  0.0222729  0.0247208   0.901 0.367922    
## matrace.factorHispanic           -0.0062221  0.0299374  -0.208 0.835419    
## matrace.factorOther              -0.0524898  0.0504267  -1.041 0.298288    
## cm1inpov                         -0.0013138  0.0039986  -0.329 0.742577    
## m1b2                             -0.0290504  0.0232972  -1.247 0.212847    
## Epithelial.cells                 -0.6103030  0.1812272  -3.368 0.000801 ***
## Leukocytes                       -3.5112384  0.1669194 -21.036  < 2e-16 ***
## birth.pm25                        0.0003696  0.0013662   0.271 0.786840    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2197 on 678 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.782,  Adjusted R-squared:  0.7785 
## F-statistic: 221.1 on 11 and 678 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm25, na.option=na.exclude, data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + 
##     birth.pm25, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.60210 -0.13868 -0.00839  0.12768  1.18987 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       3.1906659  0.3522411   9.058  < 2e-16 ***
## birth.pm10                       -0.0071641  0.0030496  -2.349   0.0191 *  
## age.dnam                          0.0345156  0.0205553   1.679   0.0936 .  
## sexm                              0.0368913  0.0161603   2.283   0.0227 *  
## matrace.factorNon-Hispanic Black -0.0148861  0.0236483  -0.629   0.5292    
## matrace.factorHispanic           -0.0504615  0.0287684  -1.754   0.0798 .  
## matrace.factorOther              -0.0790522  0.0469308  -1.684   0.0925 .  
## cm1inpov                         -0.0026756  0.0038210  -0.700   0.4840    
## m1b2                              0.0044819  0.0222010   0.202   0.8401    
## Epithelial.cells                 -0.6877474  0.1652835  -4.161 3.55e-05 ***
## Leukocytes                       -3.6235009  0.1554501 -23.310  < 2e-16 ***
## birth.pm25                       -0.0007376  0.0013347  -0.553   0.5807    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2162 on 723 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.8438, Adjusted R-squared:  0.8415 
## F-statistic: 355.1 on 11 and 723 DF,  p-value: < 2.2e-16
output$IQRcoef.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.c[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Centered & Scaled

mod<-lm(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm10, na.option=na.exclude,  data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm25.centscalestd ~ birth.pm25 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + birth.pm10, data = include, na.option = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.0625 -0.2392 -0.0300  0.1978  1.3797 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       4.7567571  0.2175050  21.870  < 2e-16 ***
## birth.pm25                       -0.0009455  0.0015270  -0.619  0.53588    
## age.dnam                         -0.0095519  0.0030880  -3.093  0.00202 ** 
## sexm                              0.0359228  0.0188347   1.907  0.05669 .  
## matrace.factorNon-Hispanic Black  0.2630261  0.0275862   9.535  < 2e-16 ***
## matrace.factorHispanic            0.0307112  0.0334776   0.917  0.35911    
## matrace.factorOther               0.0841322  0.0555139   1.516  0.12987    
## cm1inpov                          0.0095004  0.0044536   2.133  0.03308 *  
## m1b2                              0.0367066  0.0259857   1.413  0.15800    
## Epithelial.cells                 -3.0917130  0.1966652 -15.721  < 2e-16 ***
## Leukocytes                       -4.8341138  0.1835219 -26.341  < 2e-16 ***
## birth.pm10                        0.0045978  0.0034994   1.314  0.18910    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3525 on 1413 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.5517, Adjusted R-squared:  0.5482 
## F-statistic: 158.1 on 11 and 1413 DF,  p-value: < 2.2e-16
summary(mod)$coef[2,4]
## [1] 0.5358814
mod.a9<-lm(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm10, na.option=na.exclude,  data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm25.centscalestd ~ birth.pm25 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + birth.pm10, data = include[include$childteen == 
##     "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.89210 -0.22971 -0.02894  0.19033  1.39111 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       4.1456814  0.5034794   8.234 9.30e-16 ***
## birth.pm25                       -0.0010308  0.0021445  -0.481   0.6309    
## age.dnam                          0.0544160  0.0439764   1.237   0.2164    
## sexm                              0.0494740  0.0264689   1.869   0.0620 .  
## matrace.factorNon-Hispanic Black  0.2824953  0.0388026   7.280 9.26e-13 ***
## matrace.factorHispanic            0.0070163  0.0469907   0.149   0.8814    
## matrace.factorOther               0.0762463  0.0791513   0.963   0.3357    
## cm1inpov                          0.0129242  0.0062763   2.059   0.0399 *  
## m1b2                              0.0246960  0.0365679   0.675   0.4997    
## Epithelial.cells                 -2.9190522  0.2844597 -10.262  < 2e-16 ***
## Leukocytes                       -4.7593006  0.2620018 -18.165  < 2e-16 ***
## birth.pm10                        0.0004079  0.0048608   0.084   0.9332    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3449 on 678 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.5452, Adjusted R-squared:  0.5378 
## F-statistic: 73.88 on 11 and 678 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm10, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm25.centscalestd ~ birth.pm25 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + birth.pm10, data = include[include$childteen == 
##     "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.07450 -0.24539 -0.03085  0.18914  1.30270 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       4.8248954  0.5870923   8.218 9.53e-16 ***
## birth.pm25                       -0.0006265  0.0022245  -0.282   0.7783    
## age.dnam                         -0.0147225  0.0342602  -0.430   0.6675    
## sexm                              0.0242233  0.0269350   0.899   0.3688    
## matrace.factorNon-Hispanic Black  0.2488972  0.0394155   6.315 4.72e-10 ***
## matrace.factorHispanic            0.0518760  0.0479493   1.082   0.2797    
## matrace.factorOther               0.0932230  0.0782211   1.192   0.2337    
## cm1inpov                          0.0069040  0.0063686   1.084   0.2787    
## m1b2                              0.0492723  0.0370032   1.332   0.1834    
## Epithelial.cells                 -3.2127000  0.2754836 -11.662  < 2e-16 ***
## Leukocytes                       -4.8998134  0.2590941 -18.911  < 2e-16 ***
## birth.pm10                        0.0091810  0.0050829   1.806   0.0713 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3604 on 723 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.5614, Adjusted R-squared:  0.5547 
## F-statistic: 84.13 on 11 and 723 DF,  p-value: < 2.2e-16
output$IQRcoef.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.cs[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm25, na.option=na.exclude, data=include)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = peg.pm10.centscalestd ~ birth.pm10 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + birth.pm25, data = include, na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.51243 -0.11847 -0.02060  0.09357  1.57867 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       0.8191323  0.1143852   7.161 1.28e-12 ***
## birth.pm10                       -0.0043167  0.0018403  -2.346   0.0191 *  
## age.dnam                          0.0074434  0.0016240   4.583 4.98e-06 ***
## sexm                              0.0158258  0.0099051   1.598   0.1103    
## matrace.factorNon-Hispanic Black -0.0112393  0.0145075  -0.775   0.4386    
## matrace.factorHispanic           -0.0344227  0.0176058  -1.955   0.0508 .  
## matrace.factorOther              -0.0657730  0.0291946  -2.253   0.0244 *  
## cm1inpov                         -0.0022707  0.0023422  -0.969   0.3325    
## m1b2                             -0.0117182  0.0136658  -0.857   0.3913    
## Epithelial.cells                 -0.1531562  0.1034257  -1.481   0.1389    
## Leukocytes                       -0.9116961  0.0965136  -9.446  < 2e-16 ***
## birth.pm25                        0.0006197  0.0008030   0.772   0.4404    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1854 on 1413 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.3142, Adjusted R-squared:  0.3089 
## F-statistic: 58.86 on 11 and 1413 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm25, na.option=na.exclude, data=include[include$childteen=="C",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = peg.pm10.centscalestd ~ birth.pm10 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + birth.pm25, data = include[include$childteen == 
##     "C", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.49042 -0.11797 -0.02029  0.09195  1.45529 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       0.629471   0.274491   2.293   0.0221 *  
## birth.pm10                       -0.001937   0.002650  -0.731   0.4650    
## age.dnam                          0.002086   0.023975   0.087   0.9307    
## sexm                              0.014849   0.014431   1.029   0.3038    
## matrace.factorNon-Hispanic Black  0.012360   0.021155   0.584   0.5592    
## matrace.factorHispanic           -0.002265   0.025619  -0.088   0.9296    
## matrace.factorOther              -0.047822   0.043152  -1.108   0.2682    
## cm1inpov                         -0.001584   0.003422  -0.463   0.6436    
## m1b2                             -0.031446   0.019936  -1.577   0.1152    
## Epithelial.cells                  0.045747   0.155084   0.295   0.7681    
## Leukocytes                       -0.719483   0.142840  -5.037 6.07e-07 ***
## birth.pm25                        0.001032   0.001169   0.883   0.3777    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.188 on 678 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.2452, Adjusted R-squared:  0.233 
## F-statistic: 20.03 on 11 and 678 DF,  p-value: < 2.2e-16
mod.a15<-lm(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm25, na.option=na.exclude, data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = peg.pm10.centscalestd ~ birth.pm10 + age.dnam + 
##     sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + 
##     Leukocytes + birth.pm25, data = include[include$childteen == 
##     "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.45727 -0.11796 -0.01399  0.09594  1.60581 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                       7.575e-01  2.977e-01   2.545  0.01115 *  
## birth.pm10                       -7.152e-03  2.577e-03  -2.775  0.00566 ** 
## age.dnam                          2.704e-02  1.737e-02   1.556  0.12004    
## sexm                              1.639e-02  1.366e-02   1.200  0.23060    
## matrace.factorNon-Hispanic Black -3.344e-02  1.999e-02  -1.673  0.09477 .  
## matrace.factorHispanic           -6.604e-02  2.431e-02  -2.716  0.00677 ** 
## matrace.factorOther              -8.034e-02  3.966e-02  -2.026  0.04318 *  
## cm1inpov                         -2.923e-03  3.229e-03  -0.905  0.36567    
## m1b2                              6.081e-03  1.876e-02   0.324  0.74597    
## Epithelial.cells                 -3.342e-01  1.397e-01  -2.392  0.01699 *  
## Leukocytes                       -1.091e+00  1.314e-01  -8.305 4.92e-16 ***
## birth.pm25                        5.138e-05  1.128e-03   0.046  0.96368    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1828 on 723 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.352,  Adjusted R-squared:  0.3421 
## F-statistic:  35.7 on 11 and 723 DF,  p-value: < 2.2e-16
output$IQRcoef.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.cs[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Output

output
##   Exposure Age    N    IQRcoef.r    IQRlcl.r      IQRucl.r     pval.r
## 1    PM2.5 All 1425 -0.023036213 -0.06236378  0.0162913541 0.25073491
## 2    PM2.5   9  690 -0.022477644 -0.07812375  0.0331684602 0.42798410
## 3    PM2.5  15  735 -0.022530341 -0.07961788  0.0345571967 0.43869793
## 4     PM10 All 1425 -0.013418397 -0.02708334  0.0002465455 0.05427332
## 5     PM10   9  690 -0.004296392 -0.02389711  0.0153043212 0.66705312
## 6     PM10  15  735 -0.023093845 -0.04239376 -0.0037939334 0.01908291
##      IQRcoef.c    IQRlcl.c      IQRucl.c     pval.c   IQRcoef.cs   IQRlcl.cs
## 1 -0.023036213 -0.06236378  0.0162913541 0.25073491 -0.010156714 -0.04233325
## 2 -0.022477644 -0.07812375  0.0331684602 0.42798410 -0.011072399 -0.05630269
## 3 -0.022530341 -0.07961788  0.0345571967 0.43869793 -0.006730311 -0.05364322
## 4 -0.013418397 -0.02708334  0.0002465455 0.05427332 -0.013915043 -0.02555229
## 5 -0.004296392 -0.02389711  0.0153043212 0.66705312 -0.006244600 -0.02301779
## 6 -0.023093845 -0.04239376 -0.0037939334 0.01908291 -0.023054842 -0.03936636
##      IQRucl.cs     pval.cs
## 1  0.022019826 0.535881445
## 2  0.034157895 0.630914494
## 3  0.040182596 0.778288136
## 4 -0.002277794 0.019133996
## 5  0.010528587 0.465036079
## 6 -0.006743328 0.005664865
write.csv(output, file=here("Output",paste0("FFCW_AirPoll_Std_PEG_Regression_Sens_Co_Expo_", date, ".csv")) )
output<-data.frame(matrix(nrow=6, ncol= 15))
colnames(output) <- c("Exposure", "Age", "N", "IQRcoef.r", "IQRlcl.r", "IQRucl.r", "pval.r",  "IQRcoef.c", "IQRlcl.c", "IQRucl.c", "pval.c",  "IQRcoef.cs", "IQRlcl.cs", "IQRucl.cs", "pval.cs")
output$Exposure <- c(rep("PM2.5", 3), rep("PM10", 3))
output$Age <- c("All", "9", "15")

IQR(include$birth.pm25, na.rm=T)
## [1] 10.74194
IQR(include$birth.pm10, na.rm=T)
## [1] 3.223546
### Raw

mod<-lm(peg.no2.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include)
summary(mod)
## 
## Call:
## lm(formula = peg.no2.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + 
##     cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data = include)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1830 -0.5545  0.0228  0.5254  2.8673 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -0.664226   0.487213  -1.363 0.172983    
## birth.pm25                        0.003456   0.003106   1.113 0.265936    
## age.dnam                          0.006700   0.007100   0.944 0.345449    
## sexm                              0.148872   0.043526   3.420 0.000642 ***
## matrace.factorNon-Hispanic Black  0.172552   0.064501   2.675 0.007549 ** 
## matrace.factorHispanic           -0.397649   0.074028  -5.372 9.01e-08 ***
## matrace.factorOther              -0.042242   0.127519  -0.331 0.740494    
## cm1inpov                         -0.013977   0.010361  -1.349 0.177529    
## m1b2                              0.012286   0.059102   0.208 0.835355    
## Epithelial.cells                  1.152403   0.452565   2.546 0.010982 *  
## Leukocytes                        0.307356   0.422564   0.727 0.467118    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.849 on 1531 degrees of freedom
## Multiple R-squared:  0.09899,    Adjusted R-squared:  0.0931 
## F-statistic: 16.82 on 10 and 1531 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.no2.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="C",])
summary(mod.a9)
## 
## Call:
## lm(formula = peg.no2.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + 
##     cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data = include[include$childteen == 
##     "C", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2176 -0.5287  0.0272  0.5115  2.4723 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -0.892692   1.094477  -0.816 0.414973    
## birth.pm25                        0.004820   0.004508   1.069 0.285278    
## age.dnam                          0.011502   0.094046   0.122 0.902692    
## sexm                              0.066613   0.060709   1.097 0.272894    
## matrace.factorNon-Hispanic Black  0.220977   0.090075   2.453 0.014387 *  
## matrace.factorHispanic           -0.348151   0.103986  -3.348 0.000855 ***
## matrace.factorOther               0.015306   0.178633   0.086 0.931741    
## cm1inpov                         -0.011259   0.014503  -0.776 0.437825    
## m1b2                              0.047757   0.082623   0.578 0.563430    
## Epithelial.cells                  1.494490   0.654160   2.285 0.022620 *  
## Leukocytes                        0.370512   0.603846   0.614 0.539677    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8251 on 738 degrees of freedom
## Multiple R-squared:  0.1075, Adjusted R-squared:  0.09543 
## F-statistic: 8.892 on 10 and 738 DF,  p-value: 6.839e-14
mod.a15<-lm(peg.no2.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="T",])
summary(mod.a15)
## 
## Call:
## lm(formula = peg.no2.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + 
##     cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data = include[include$childteen == 
##     "T", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.94207 -0.59447  0.03531  0.57857  2.96218 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -1.748154   1.210061  -1.445 0.148948    
## birth.pm25                        0.002848   0.004494   0.634 0.526510    
## age.dnam                          0.090877   0.065027   1.398 0.162650    
## sexm                              0.221763   0.062555   3.545 0.000416 ***
## matrace.factorNon-Hispanic Black  0.123440   0.093017   1.327 0.184873    
## matrace.factorHispanic           -0.450128   0.106997  -4.207 2.89e-05 ***
## matrace.factorOther              -0.080511   0.182187  -0.442 0.658672    
## cm1inpov                         -0.014422   0.014947  -0.965 0.334906    
## m1b2                             -0.014874   0.084878  -0.175 0.860940    
## Epithelial.cells                  0.869083   0.634907   1.369 0.171444    
## Leukocytes                        0.184966   0.597113   0.310 0.756821    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8723 on 782 degrees of freedom
## Multiple R-squared:  0.09896,    Adjusted R-squared:  0.08744 
## F-statistic: 8.589 on 10 and 782 DF,  p-value: 2.123e-13
output$IQRcoef.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$N[1:3] <- c(nrow(model.frame(mod)), nrow(model.frame(mod.a9)), nrow(model.frame(mod.a15)))

output$IQRlcl.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.r[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.r[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.no2.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include)
summary(mod)
## 
## Call:
## lm(formula = peg.no2.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + 
##     cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data = include)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.05854 -0.54561  0.01599  0.52896  2.90550 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -1.306254   0.510290  -2.560 0.010576 *  
## birth.pm10                        0.042167   0.008098   5.207  2.2e-07 ***
## age.dnam                          0.007762   0.007318   1.061 0.289036    
## sexm                              0.152299   0.044638   3.412 0.000663 ***
## matrace.factorNon-Hispanic Black  0.139726   0.064978   2.150 0.031697 *  
## matrace.factorHispanic           -0.287773   0.077981  -3.690 0.000232 ***
## matrace.factorOther              -0.029005   0.131584  -0.220 0.825570    
## cm1inpov                         -0.010747   0.010554  -1.018 0.308754    
## m1b2                              0.017284   0.061581   0.281 0.779000    
## Epithelial.cells                  1.248591   0.466175   2.678 0.007484 ** 
## Leukocytes                        0.379386   0.435015   0.872 0.383290    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8355 on 1414 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.1161, Adjusted R-squared:  0.1098 
## F-statistic: 18.56 on 10 and 1414 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.no2.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="C",])
summary(mod.a9)
## 
## Call:
## lm(formula = peg.no2.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + 
##     cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data = include[include$childteen == 
##     "C", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.10860 -0.53508  0.01444  0.47567  2.46999 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -1.864030   1.165512  -1.599 0.110214    
## birth.pm10                        0.043011   0.011188   3.844 0.000132 ***
## age.dnam                          0.077236   0.102916   0.750 0.453226    
## sexm                              0.066435   0.062204   1.068 0.285897    
## matrace.factorNon-Hispanic Black  0.206750   0.090609   2.282 0.022811 *  
## matrace.factorHispanic           -0.229851   0.108614  -2.116 0.034690 *  
## matrace.factorOther               0.025174   0.186042   0.135 0.892404    
## cm1inpov                         -0.005561   0.014752  -0.377 0.706300    
## m1b2                              0.060921   0.085949   0.709 0.478690    
## Epithelial.cells                  1.260604   0.668683   1.885 0.059829 .  
## Leukocytes                        0.189314   0.615771   0.307 0.758602    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8108 on 679 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.1254, Adjusted R-squared:  0.1125 
## F-statistic: 9.735 on 10 and 679 DF,  p-value: 2.696e-15
mod.a15<-lm(peg.no2.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="T",])
summary(mod.a15)
## 
## Call:
## lm(formula = peg.no2.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + 
##     cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data = include[include$childteen == 
##     "T", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.08166 -0.56316  0.01064  0.57230  3.00735 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -3.13399    1.39380  -2.249 0.024843 *  
## birth.pm10                        0.03942    0.01186   3.323 0.000935 ***
## age.dnam                          0.13078    0.08030   1.629 0.103849    
## sexm                              0.22609    0.06419   3.522 0.000455 ***
## matrace.factorNon-Hispanic Black  0.07770    0.09346   0.831 0.406012    
## matrace.factorHispanic           -0.34444    0.11216  -3.071 0.002214 ** 
## matrace.factorOther              -0.06147    0.18638  -0.330 0.741628    
## cm1inpov                         -0.01273    0.01517  -0.839 0.401610    
## m1b2                             -0.02351    0.08815  -0.267 0.789808    
## Epithelial.cells                  1.20426    0.65641   1.835 0.066972 .  
## Leukocytes                        0.45539    0.61739   0.738 0.460994    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8589 on 724 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.117,  Adjusted R-squared:  0.1048 
## F-statistic: 9.591 on 10 and 724 DF,  p-value: 4.143e-15
output$N[4:6] <- c(nrow(model.frame(mod)), nrow(model.frame(mod.a9)), nrow(model.frame(mod.a15)))

output$IQRcoef.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.r[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Centered

mod<-lm(peg.no2.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include)
summary(mod)
## 
## Call:
## lm(formula = peg.no2.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1830 -0.5545  0.0228  0.5254  2.8673 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -0.664226   0.487213  -1.363 0.172983    
## birth.pm25                        0.003456   0.003106   1.113 0.265936    
## age.dnam                          0.006700   0.007100   0.944 0.345449    
## sexm                              0.148872   0.043526   3.420 0.000642 ***
## matrace.factorNon-Hispanic Black  0.172552   0.064501   2.675 0.007549 ** 
## matrace.factorHispanic           -0.397649   0.074028  -5.372 9.01e-08 ***
## matrace.factorOther              -0.042242   0.127519  -0.331 0.740494    
## cm1inpov                         -0.013977   0.010361  -1.349 0.177529    
## m1b2                              0.012286   0.059102   0.208 0.835355    
## Epithelial.cells                  1.152403   0.452565   2.546 0.010982 *  
## Leukocytes                        0.307356   0.422564   0.727 0.467118    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.849 on 1531 degrees of freedom
## Multiple R-squared:  0.09899,    Adjusted R-squared:  0.0931 
## F-statistic: 16.82 on 10 and 1531 DF,  p-value: < 2.2e-16
summary(mod)$coef[2,4]
## [1] 0.2659356
mod.a9<-lm(peg.no2.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="C",])
summary(mod.a9)
## 
## Call:
## lm(formula = peg.no2.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "C", ])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2176 -0.5287  0.0272  0.5115  2.4723 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -0.892692   1.094477  -0.816 0.414973    
## birth.pm25                        0.004820   0.004508   1.069 0.285278    
## age.dnam                          0.011502   0.094046   0.122 0.902692    
## sexm                              0.066613   0.060709   1.097 0.272894    
## matrace.factorNon-Hispanic Black  0.220977   0.090075   2.453 0.014387 *  
## matrace.factorHispanic           -0.348151   0.103986  -3.348 0.000855 ***
## matrace.factorOther               0.015306   0.178633   0.086 0.931741    
## cm1inpov                         -0.011259   0.014503  -0.776 0.437825    
## m1b2                              0.047757   0.082623   0.578 0.563430    
## Epithelial.cells                  1.494490   0.654160   2.285 0.022620 *  
## Leukocytes                        0.370512   0.603846   0.614 0.539677    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8251 on 738 degrees of freedom
## Multiple R-squared:  0.1075, Adjusted R-squared:  0.09543 
## F-statistic: 8.892 on 10 and 738 DF,  p-value: 6.839e-14
mod.a15<-lm(peg.no2.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="T",])
summary(mod.a15)
## 
## Call:
## lm(formula = peg.no2.centstd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "T", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.94207 -0.59447  0.03531  0.57857  2.96218 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -1.748154   1.210061  -1.445 0.148948    
## birth.pm25                        0.002848   0.004494   0.634 0.526510    
## age.dnam                          0.090877   0.065027   1.398 0.162650    
## sexm                              0.221763   0.062555   3.545 0.000416 ***
## matrace.factorNon-Hispanic Black  0.123440   0.093017   1.327 0.184873    
## matrace.factorHispanic           -0.450128   0.106997  -4.207 2.89e-05 ***
## matrace.factorOther              -0.080511   0.182187  -0.442 0.658672    
## cm1inpov                         -0.014422   0.014947  -0.965 0.334906    
## m1b2                             -0.014874   0.084878  -0.175 0.860940    
## Epithelial.cells                  0.869083   0.634907   1.369 0.171444    
## Leukocytes                        0.184966   0.597113   0.310 0.756821    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8723 on 782 degrees of freedom
## Multiple R-squared:  0.09896,    Adjusted R-squared:  0.08744 
## F-statistic: 8.589 on 10 and 782 DF,  p-value: 2.123e-13
output$IQRcoef.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.c[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.c[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.no2.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include)
summary(mod)
## 
## Call:
## lm(formula = peg.no2.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.05854 -0.54561  0.01599  0.52896  2.90550 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -1.306254   0.510290  -2.560 0.010576 *  
## birth.pm10                        0.042167   0.008098   5.207  2.2e-07 ***
## age.dnam                          0.007762   0.007318   1.061 0.289036    
## sexm                              0.152299   0.044638   3.412 0.000663 ***
## matrace.factorNon-Hispanic Black  0.139726   0.064978   2.150 0.031697 *  
## matrace.factorHispanic           -0.287773   0.077981  -3.690 0.000232 ***
## matrace.factorOther              -0.029005   0.131584  -0.220 0.825570    
## cm1inpov                         -0.010747   0.010554  -1.018 0.308754    
## m1b2                              0.017284   0.061581   0.281 0.779000    
## Epithelial.cells                  1.248591   0.466175   2.678 0.007484 ** 
## Leukocytes                        0.379386   0.435015   0.872 0.383290    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8355 on 1414 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.1161, Adjusted R-squared:  0.1098 
## F-statistic: 18.56 on 10 and 1414 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.no2.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="C",])
summary(mod.a9)
## 
## Call:
## lm(formula = peg.no2.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "C", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.10860 -0.53508  0.01444  0.47567  2.46999 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -1.864030   1.165512  -1.599 0.110214    
## birth.pm10                        0.043011   0.011188   3.844 0.000132 ***
## age.dnam                          0.077236   0.102916   0.750 0.453226    
## sexm                              0.066435   0.062204   1.068 0.285897    
## matrace.factorNon-Hispanic Black  0.206750   0.090609   2.282 0.022811 *  
## matrace.factorHispanic           -0.229851   0.108614  -2.116 0.034690 *  
## matrace.factorOther               0.025174   0.186042   0.135 0.892404    
## cm1inpov                         -0.005561   0.014752  -0.377 0.706300    
## m1b2                              0.060921   0.085949   0.709 0.478690    
## Epithelial.cells                  1.260604   0.668683   1.885 0.059829 .  
## Leukocytes                        0.189314   0.615771   0.307 0.758602    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8108 on 679 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.1254, Adjusted R-squared:  0.1125 
## F-statistic: 9.735 on 10 and 679 DF,  p-value: 2.696e-15
mod.a15<-lm(peg.no2.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="T",])
summary(mod.a15)
## 
## Call:
## lm(formula = peg.no2.centstd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "T", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3.08166 -0.56316  0.01064  0.57230  3.00735 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -3.13399    1.39380  -2.249 0.024843 *  
## birth.pm10                        0.03942    0.01186   3.323 0.000935 ***
## age.dnam                          0.13078    0.08030   1.629 0.103849    
## sexm                              0.22609    0.06419   3.522 0.000455 ***
## matrace.factorNon-Hispanic Black  0.07770    0.09346   0.831 0.406012    
## matrace.factorHispanic           -0.34444    0.11216  -3.071 0.002214 ** 
## matrace.factorOther              -0.06147    0.18638  -0.330 0.741628    
## cm1inpov                         -0.01273    0.01517  -0.839 0.401610    
## m1b2                             -0.02351    0.08815  -0.267 0.789808    
## Epithelial.cells                  1.20426    0.65641   1.835 0.066972 .  
## Leukocytes                        0.45539    0.61739   0.738 0.460994    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8589 on 724 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.117,  Adjusted R-squared:  0.1048 
## F-statistic: 9.591 on 10 and 724 DF,  p-value: 4.143e-15
output$IQRcoef.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.c[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.c[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Centered & Scaled

mod<-lm(peg.no2.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include)
summary(mod)
## 
## Call:
## lm(formula = peg.no2.centscalestd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.30172 -0.46377 -0.00722  0.41788  2.25901 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -1.8488792  0.3958191  -4.671 3.26e-06 ***
## birth.pm25                        0.0011752  0.0025231   0.466   0.6414    
## age.dnam                          0.0002081  0.0057679   0.036   0.9712    
## sexm                              0.0747058  0.0353614   2.113   0.0348 *  
## matrace.factorNon-Hispanic Black  0.1311720  0.0524018   2.503   0.0124 *  
## matrace.factorHispanic           -0.2547912  0.0601414  -4.237 2.41e-05 ***
## matrace.factorOther               0.0088049  0.1035980   0.085   0.9323    
## cm1inpov                         -0.0084731  0.0084172  -1.007   0.3143    
## m1b2                             -0.0027523  0.0480155  -0.057   0.9543    
## Epithelial.cells                  1.5992350  0.3676708   4.350 1.45e-05 ***
## Leukocytes                        1.6185804  0.3432978   4.715 2.64e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6897 on 1531 degrees of freedom
## Multiple R-squared:  0.06175,    Adjusted R-squared:  0.05562 
## F-statistic: 10.08 on 10 and 1531 DF,  p-value: < 2.2e-16
summary(mod)$coef[2,4]
## [1] 0.6414344
mod.a9<-lm(peg.no2.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="C",])
summary(mod.a9)
## 
## Call:
## lm(formula = peg.no2.centscalestd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "C", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.99697 -0.43871 -0.03127  0.38522  2.04435 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -2.669007   0.892013  -2.992 0.002863 ** 
## birth.pm25                        0.004219   0.003674   1.148 0.251245    
## age.dnam                          0.048201   0.076649   0.629 0.529634    
## sexm                              0.019338   0.049479   0.391 0.696030    
## matrace.factorNon-Hispanic Black  0.180980   0.073412   2.465 0.013918 *  
## matrace.factorHispanic           -0.218473   0.084750  -2.578 0.010135 *  
## matrace.factorOther               0.064840   0.145589   0.445 0.656185    
## cm1inpov                         -0.004628   0.011820  -0.392 0.695493    
## m1b2                              0.026967   0.067339   0.400 0.688930    
## Epithelial.cells                  2.004571   0.533149   3.760 0.000183 ***
## Leukocytes                        1.815548   0.492142   3.689 0.000242 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6725 on 738 degrees of freedom
## Multiple R-squared:  0.07172,    Adjusted R-squared:  0.05914 
## F-statistic: 5.702 on 10 and 738 DF,  p-value: 2.873e-08
mod.a15<-lm(peg.no2.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="T",])
summary(mod.a15)
## 
## Call:
## lm(formula = peg.no2.centscalestd ~ birth.pm25 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "T", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.36812 -0.48011 -0.01618  0.44083  2.32274 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -2.3970113  0.9803638  -2.445 0.014704 *  
## birth.pm25                       -0.0006332  0.0036412  -0.174 0.861994    
## age.dnam                          0.0594826  0.0526832   1.129 0.259218    
## sexm                              0.1250123  0.0506804   2.467 0.013851 *  
## matrace.factorNon-Hispanic Black  0.0812555  0.0753603   1.078 0.281265    
## matrace.factorHispanic           -0.3006442  0.0866863  -3.468 0.000553 ***
## matrace.factorOther              -0.0335218  0.1476035  -0.227 0.820400    
## cm1inpov                         -0.0102014  0.0121093  -0.842 0.399799    
## m1b2                             -0.0250722  0.0687664  -0.365 0.715509    
## Epithelial.cells                  1.2482404  0.5143876   2.427 0.015464 *  
## Leukocytes                        1.3852411  0.4837677   2.863 0.004303 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7067 on 782 degrees of freedom
## Multiple R-squared:  0.06265,    Adjusted R-squared:  0.05066 
## F-statistic: 5.226 on 10 and 782 DF,  p-value: 1.862e-07
output$IQRcoef.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.cs[1:3] <- IQR(include$birth.pm25, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.cs[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(peg.no2.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include)
summary(mod)
## 
## Call:
## lm(formula = peg.no2.centscalestd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.40944 -0.46484 -0.00787  0.42322  2.29614 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -2.3836205  0.4174824  -5.710 1.38e-08 ***
## birth.pm10                        0.0315930  0.0066253   4.769 2.05e-06 ***
## age.dnam                          0.0005325  0.0059874   0.089  0.92915    
## sexm                              0.0716567  0.0365199   1.962  0.04994 *  
## matrace.factorNon-Hispanic Black  0.0956426  0.0531605   1.799  0.07221 .  
## matrace.factorHispanic           -0.1897668  0.0637983  -2.974  0.00298 ** 
## matrace.factorOther               0.0237347  0.1076523   0.220  0.82553    
## cm1inpov                         -0.0072938  0.0086348  -0.845  0.39842    
## m1b2                             -0.0062045  0.0503813  -0.123  0.90201    
## Epithelial.cells                  1.7122865  0.3813907   4.490 7.72e-06 ***
## Leukocytes                        1.7128364  0.3558979   4.813 1.65e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6835 on 1414 degrees of freedom
##   (117 observations deleted due to missingness)
## Multiple R-squared:  0.07681,    Adjusted R-squared:  0.07028 
## F-statistic: 11.76 on 10 and 1414 DF,  p-value: < 2.2e-16
mod.a9<-lm(peg.no2.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="C",])
summary(mod.a9)
## 
## Call:
## lm(formula = peg.no2.centscalestd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "C", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.91032 -0.43732 -0.02557  0.38024  2.10800 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -3.343310   0.954806  -3.502 0.000493 ***
## birth.pm10                        0.033966   0.009165   3.706 0.000228 ***
## age.dnam                          0.086506   0.084311   1.026 0.305241    
## sexm                              0.014025   0.050959   0.275 0.783231    
## matrace.factorNon-Hispanic Black  0.163745   0.074228   2.206 0.027721 *  
## matrace.factorHispanic           -0.134588   0.088979  -1.513 0.130850    
## matrace.factorOther               0.077781   0.152408   0.510 0.609975    
## cm1inpov                         -0.001996   0.012085  -0.165 0.868842    
## m1b2                              0.027099   0.070411   0.385 0.700457    
## Epithelial.cells                  1.900791   0.547796   3.470 0.000554 ***
## Leukocytes                        1.739196   0.504450   3.448 0.000600 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6642 on 679 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.08932,    Adjusted R-squared:  0.07591 
## F-statistic:  6.66 on 10 and 679 DF,  p-value: 6.568e-10
mod.a15<-lm(peg.no2.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, data=include[include$childteen=="T",])
summary(mod.a15)
## 
## Call:
## lm(formula = peg.no2.centscalestd ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, 
##     data = include[include$childteen == "T", ])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.47096 -0.47661 -0.01199  0.45934  2.36221 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                      -3.132700   1.140287  -2.747  0.00616 **
## birth.pm10                        0.028529   0.009706   2.939  0.00339 **
## age.dnam                          0.065364   0.065699   0.995  0.32011   
## sexm                              0.123385   0.052513   2.350  0.01906 * 
## matrace.factorNon-Hispanic Black  0.035567   0.076457   0.465  0.64194   
## matrace.factorHispanic           -0.244662   0.091761  -2.666  0.00784 **
## matrace.factorOther              -0.016594   0.152484  -0.109  0.91337   
## cm1inpov                         -0.010232   0.012414  -0.824  0.41006   
## m1b2                             -0.037833   0.072116  -0.525  0.60001   
## Epithelial.cells                  1.518806   0.537019   2.828  0.00481 **
## Leukocytes                        1.610886   0.505096   3.189  0.00149 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7027 on 724 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.07534,    Adjusted R-squared:  0.06257 
## F-statistic: 5.899 on 10 and 724 DF,  p-value: 1.322e-08
output$IQRcoef.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.cs[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.cs[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Output

output
##   Exposure Age    N  IQRcoef.r    IQRlcl.r  IQRucl.r       pval.r  IQRcoef.c
## 1    PM2.5 All 1542 0.03712733 -0.02831184 0.1025665 2.659356e-01 0.03712733
## 2    PM2.5   9  749 0.05177918 -0.04328371 0.1468421 2.852780e-01 0.05177918
## 3    PM2.5  15  793 0.03059005 -0.06417935 0.1253594 5.265104e-01 0.03059005
## 4     PM10 All 1425 0.13592796  0.08471987 0.1871360 2.202121e-07 0.13592796
## 5     PM10   9  690 0.13864855  0.06783590 0.2094612 1.322082e-04 0.13864855
## 6     PM10  15  735 0.12708576  0.05200450 0.2021670 9.350787e-04 0.12708576
##      IQRlcl.c  IQRucl.c       pval.c   IQRcoef.cs   IQRlcl.cs  IQRucl.cs
## 1 -0.02831184 0.1025665 2.659356e-01  0.012624418 -0.04053935 0.06578819
## 2 -0.04328371 0.1468421 2.852780e-01  0.045315217 -0.03216229 0.12279272
## 3 -0.06417935 0.1253594 5.265104e-01 -0.006801588 -0.08358161 0.06997843
## 4  0.08471987 0.1871360 2.202121e-07  0.101841337  0.05994654 0.14373613
## 5  0.06783590 0.2094612 1.322082e-04  0.109489883  0.05147904 0.16750072
## 6  0.05200450 0.2021670 9.350787e-04  0.091963187  0.03053807 0.15338830
##        pval.cs
## 1 6.414344e-01
## 2 2.512452e-01
## 3 8.619943e-01
## 4 2.047482e-06
## 5 2.278436e-04
## 6 3.394323e-03
write.csv(output, file=here("Output",paste0("FFCW_AirPoll_Std_PEG_Regression_NO2_Sens_", date, ".csv")) )
output<-data.frame(matrix(nrow=6, ncol= 15))
colnames(output) <- c("Exposure", "cg", "N", "IQRcoef.r", "IQRlcl.r", "IQRucl.r", "pval.r",  "IQRcoef.c", "IQRlcl.c", "IQRucl.c", "pval.c",  "IQRcoef.cs", "IQRlcl.cs", "IQRucl.cs", "pval.cs")
output$Exposure <- c(rep("PM10", 6))
output$cg <- c("cg00905156", "cg06849931", "cg15082635", "cg18640183", "cg20340716", "cg24127244")

IQR(include$birth.pm10, na.rm=T)
## [1] 3.223546
### Raw

mod<-lm(cg00905156.percent ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2.factor + Epithelial.cells + Leukocytes, na.option=na.exclude, data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = cg00905156.percent ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2.factor + Epithelial.cells + 
##     Leukocytes, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6818 -0.7848 -0.2941  0.4620 25.4124 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                      -0.459437   2.633850  -0.174   0.8616  
## birth.pm10                       -0.015013   0.022559  -0.665   0.5060  
## age.dnam                          0.063853   0.152700   0.418   0.6760  
## sexm                              0.005107   0.122053   0.042   0.9666  
## matrace.factorNon-Hispanic Black -0.035106   0.177706  -0.198   0.8435  
## matrace.factorHispanic           -0.003828   0.213275  -0.018   0.9857  
## matrace.factorOther              -0.356310   0.354412  -1.005   0.3151  
## cm1inpov                         -0.045678   0.028853  -1.583   0.1138  
## m1b2.factorNot Married           -0.207580   0.167615  -1.238   0.2160  
## Epithelial.cells                  1.312004   1.248171   1.051   0.2935  
## Leukocytes                        2.599711   1.173973   2.214   0.0271 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.633 on 724 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.02861,    Adjusted R-squared:  0.01519 
## F-statistic: 2.132 on 10 and 724 DF,  p-value: 0.02019
mod.a9<-lm(cg06849931.percent ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2.factor + Epithelial.cells + Leukocytes, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = cg06849931.percent ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2.factor + Epithelial.cells + 
##     Leukocytes, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.6120  -2.9204   0.2248   2.8225  14.3182 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      -17.16747    7.15281  -2.400   0.0166 *  
## birth.pm10                         0.04948    0.06126   0.808   0.4195    
## age.dnam                          -0.31557    0.41469  -0.761   0.4469    
## sexm                              -0.79892    0.33146  -2.410   0.0162 *  
## matrace.factorNon-Hispanic Black  -0.37994    0.48260  -0.787   0.4314    
## matrace.factorHispanic            -0.06272    0.57920  -0.108   0.9138    
## matrace.factorOther                0.55109    0.96249   0.573   0.5671    
## cm1inpov                           0.02989    0.07836   0.381   0.7030    
## m1b2.factorNot Married             0.08895    0.45520   0.195   0.8451    
## Epithelial.cells                  13.45759    3.38969   3.970  7.9e-05 ***
## Leukocytes                        94.56616    3.18819  29.661  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.435 on 724 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.9061, Adjusted R-squared:  0.9048 
## F-statistic: 698.4 on 10 and 724 DF,  p-value: < 2.2e-16
mod.a15<-lm(cg15082635.percent ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2.factor + Epithelial.cells + Leukocytes, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = cg15082635.percent ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2.factor + Epithelial.cells + 
##     Leukocytes, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5459 -0.5524 -0.2337  0.3183  4.7566 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                      -0.285277   1.412659  -0.202   0.8400  
## birth.pm10                       -0.002744   0.012100  -0.227   0.8207  
## age.dnam                          0.080975   0.081900   0.989   0.3231  
## sexm                              0.077026   0.065463   1.177   0.2397  
## matrace.factorNon-Hispanic Black -0.213750   0.095312  -2.243   0.0252 *
## matrace.factorHispanic           -0.228922   0.114390  -2.001   0.0457 *
## matrace.factorOther              -0.453005   0.190088  -2.383   0.0174 *
## cm1inpov                         -0.037550   0.015475  -2.426   0.0155 *
## m1b2.factorNot Married            0.023873   0.089900   0.266   0.7907  
## Epithelial.cells                  0.502920   0.669453   0.751   0.4528  
## Leukocytes                        1.374138   0.629658   2.182   0.0294 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.876 on 724 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.0535, Adjusted R-squared:  0.04043 
## F-statistic: 4.093 on 10 and 724 DF,  p-value: 1.674e-05
output$N[1:3] <- c(nrow(model.frame(mod)), nrow(model.frame(mod.a9)), nrow(model.frame(mod.a15)))

output$IQRcoef.r[1:3] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.r[1:3] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.r[1:3] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.r[1:3] <-c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

mod<-lm(cg18640183.percent ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2.factor + Epithelial.cells + Leukocytes, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod)
## 
## Call:
## lm(formula = cg18640183.percent ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2.factor + Epithelial.cells + 
##     Leukocytes, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1911 -0.8247 -0.2455  0.6078  7.2358 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                       5.306206   1.939296   2.736  0.00637 **
## birth.pm10                       -0.036864   0.016610  -2.219  0.02677 * 
## age.dnam                          0.159695   0.112433   1.420  0.15593   
## sexm                              0.045519   0.089867   0.507  0.61265   
## matrace.factorNon-Hispanic Black -0.246146   0.130844  -1.881  0.06034 . 
## matrace.factorHispanic           -0.271892   0.157034  -1.731  0.08380 . 
## matrace.factorOther              -0.433517   0.260952  -1.661  0.09709 . 
## cm1inpov                         -0.009308   0.021244  -0.438  0.66142   
## m1b2.factorNot Married            0.020652   0.123415   0.167  0.86715   
## Epithelial.cells                 -1.142799   0.919024  -1.243  0.21409   
## Leukocytes                       -2.083069   0.864393  -2.410  0.01621 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.203 on 724 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.03549,    Adjusted R-squared:  0.02216 
## F-statistic: 2.664 on 10 and 724 DF,  p-value: 0.003354
mod.a9<-lm(cg20340716.percent ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2.factor + Epithelial.cells + Leukocytes, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a9)
## 
## Call:
## lm(formula = cg20340716.percent ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2.factor + Epithelial.cells + 
##     Leukocytes, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6644 -0.8078  0.0979  0.9102  3.1862 
## 
## Coefficients:
##                                  Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      93.23108    2.01395  46.293  < 2e-16 ***
## birth.pm10                        0.04188    0.01725   2.428  0.01542 *  
## age.dnam                         -0.12456    0.11676  -1.067  0.28640    
## sexm                             -0.03623    0.09333  -0.388  0.69794    
## matrace.factorNon-Hispanic Black  0.07472    0.13588   0.550  0.58257    
## matrace.factorHispanic            0.37330    0.16308   2.289  0.02236 *  
## matrace.factorOther               0.02934    0.27100   0.108  0.91380    
## cm1inpov                         -0.03718    0.02206  -1.685  0.09240 .  
## m1b2.factorNot Married           -0.19496    0.12817  -1.521  0.12866    
## Epithelial.cells                  2.75288    0.95440   2.884  0.00404 ** 
## Leukocytes                        0.86208    0.89767   0.960  0.33720    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.249 on 724 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.07091,    Adjusted R-squared:  0.05807 
## F-statistic: 5.525 on 10 and 724 DF,  p-value: 5.912e-08
mod.a15<-lm(cg24127244.percent ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2.factor + Epithelial.cells + Leukocytes, na.option=na.exclude,  data=include[include$childteen=="T",])
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
##  extra argument 'na.option' will be disregarded
summary(mod.a15)
## 
## Call:
## lm(formula = cg24127244.percent ~ birth.pm10 + age.dnam + sex + 
##     matrace.factor + cm1inpov + m1b2.factor + Epithelial.cells + 
##     Leukocytes, data = include[include$childteen == "T", ], na.option = na.exclude)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6055 -0.4770 -0.1223  0.3112  2.8966 
## 
## Coefficients:
##                                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                       2.001030   1.128445   1.773  0.07661 . 
## birth.pm10                       -0.004704   0.009665  -0.487  0.62666   
## age.dnam                         -0.036054   0.065423  -0.551  0.58174   
## sexm                             -0.052209   0.052292  -0.998  0.31842   
## matrace.factorNon-Hispanic Black -0.151162   0.076136  -1.985  0.04747 * 
## matrace.factorHispanic           -0.184366   0.091376  -2.018  0.04399 * 
## matrace.factorOther              -0.185906   0.151844  -1.224  0.22123   
## cm1inpov                         -0.019495   0.012362  -1.577  0.11522   
## m1b2.factorNot Married           -0.014424   0.071813  -0.201  0.84087   
## Epithelial.cells                  0.605106   0.534765   1.132  0.25820   
## Leukocytes                        1.385943   0.502976   2.755  0.00601 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6997 on 724 degrees of freedom
##   (58 observations deleted due to missingness)
## Multiple R-squared:  0.05462,    Adjusted R-squared:  0.04156 
## F-statistic: 4.183 on 10 and 724 DF,  p-value: 1.181e-05
output$N[4:6] <- c(nrow(model.frame(mod)), nrow(model.frame(mod.a9)), nrow(model.frame(mod.a15)))

output$IQRcoef.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(mod$coefficients[2], mod.a9$coefficients[2], mod.a15$coefficients[2])

output$IQRlcl.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,1], confint(mod.a9)[2,1], confint(mod.a15)[2,1])

output$IQRucl.r[4:6] <- IQR(include$birth.pm10, na.rm=T) * c(confint(mod)[2,2], confint(mod.a9)[2,2], confint(mod.a15)[2,2])

output$pval.r[4:6] <- c(summary(mod)$coef[2,4], summary(mod.a9)$coef[2,4], summary(mod.a15)$coef[2,4])

### Output

output
##   Exposure         cg   N    IQRcoef.r    IQRlcl.r    IQRucl.r    pval.r
## 1     PM10 cg00905156 735 -0.048393632 -0.19116133  0.09437406 0.5059575
## 2     PM10 cg06849931 735  0.159508407 -0.22820947  0.54722629 0.4195367
## 3     PM10 cg15082635 735 -0.008844694 -0.08541779  0.06772840 0.8206683
## 4     PM10 cg18640183 735 -0.118834270 -0.22395368 -0.01371486 0.0267694
## 5     PM10 cg20340716 735  0.135010777  0.02584494  0.24417661 0.0154232
## 6     PM10 cg24127244 735 -0.015162089 -0.07632940  0.04600522 0.6266555
##   IQRcoef.c IQRlcl.c IQRucl.c pval.c IQRcoef.cs IQRlcl.cs IQRucl.cs pval.cs
## 1        NA       NA       NA     NA         NA        NA        NA      NA
## 2        NA       NA       NA     NA         NA        NA        NA      NA
## 3        NA       NA       NA     NA         NA        NA        NA      NA
## 4        NA       NA       NA     NA         NA        NA        NA      NA
## 5        NA       NA       NA     NA         NA        NA        NA      NA
## 6        NA       NA       NA     NA         NA        NA        NA      NA
write.csv(output, file=here("Output",paste0("FFCW_AirPoll_SingleSite_Regression_Sens_Age15_PM10_", date, ".csv")) )
head(include) # Use idnum as my random effect. Random intercept only.
##     idnum            MethID childteen ck6ethrace cm1ethrace ethrace m1city
## 1 1000002 3999984114_R01C02         T          2          2       2      1
## 2 1000002 3999984114_R02C02         C          2          2       2      1
## 3 1000005 3999932091_R04C02         C          2          2       2      1
## 4 1000005 3999932092_R04C02         T          2          2       2      1
## 5 1000019 3999932091_R01C01         C          2          2       2      1
## 6 1000019 3999932092_R01C01         T          2          2       2      1
##   cm1inpov probe_fail_pct sex age plate cut    immune    epithelial cm1edu m1b2
## 1      2.4    0.005142331   f 194     2     0.8119736  1.880264e-01      1    2
## 2      2.4    0.022177233   f 117     2     1.0000000 -1.387779e-17      1    2
## 3      0.5    0.037646589   f 116     6     1.0000000  0.000000e+00      2    2
## 4      0.5    0.014520544   f 194     6     0.8172765  1.827235e-01      2    2
## 5      0.3    0.021679759   m 118     6     1.0000000  0.000000e+00      1    2
## 6      0.3    0.024064100   m 200     6     1.0000000  0.000000e+00      1    2
##   peg.pm25.raw peg.pm10.raw  peg.no2.raw peg.pm25.cent peg.pm10.cent
## 1  -0.01197784 -0.002321927 -0.004139335  5.981487e-04  1.297578e-04
## 2  -0.01221065 -0.002468995 -0.003662677  3.653364e-04 -1.731028e-05
## 3  -0.01260724 -0.002521653 -0.004819069 -3.125533e-05 -6.996767e-05
## 4  -0.01230936 -0.002361226 -0.005083212  2.666311e-04  9.045947e-05
## 5  -0.01245799 -0.002563784 -0.003486199  1.179929e-04 -1.120985e-04
## 6  -0.01275699 -0.002626098 -0.002173911 -1.810042e-04 -1.744129e-04
##   peg.no2.cent peg.pm25.centscale peg.pm10.centscale peg.no2.centscale
## 1 1.005331e-03        0.006359692       5.492113e-04       0.014495957
## 2 1.481989e-03        0.003628842      -3.870897e-04       0.022967084
## 3 3.255975e-04       -0.002516903      -2.381435e-03       0.007047334
## 4 6.145456e-05        0.001455718      -3.849743e-05       0.003577413
## 5 1.658467e-03        0.002407788      -9.189505e-04       0.030509673
## 6 2.970755e-03       -0.003497852      -1.530059e-03       0.049893402
##   cg18640183 cg24127244 cg00905156 cg15082635 cg06849931 cg20340716
## 1 0.05230433 0.02252754 0.02103662 0.02077820  0.5820260  0.9282739
## 2 0.05154276 0.02162791 0.02367230 0.01468084  0.7376557  0.9214317
## 3 0.03810300 0.01497054 0.01343715 0.01371734  0.7065934  0.9476437
## 4 0.04436995 0.02591302 0.02153345 0.01652054  0.6033457  0.9331084
## 5 0.05221866 0.02034587 0.01835185 0.01634140  0.8437224  0.9136594
## 6 0.04878337 0.01992653 0.01950365 0.01465849  0.8974772  0.9157463
##   Epithelial.cells Leukocytes  age.dnam peg.pm25.rawstd peg.pm10.rawstd
## 1     1.721681e-01  0.7876077 16.166667      1.01022672      0.50563793
## 2    -1.387779e-17  0.9908079  9.750000      0.61702476     -0.06745438
## 3     0.000000e+00  1.0385861  9.666667     -0.05278783     -0.27264880
## 4     1.804163e-01  0.8137317 16.166667      0.45031912      0.35250088
## 5     0.000000e+00  1.0904245  9.833333      0.19928082     -0.43682360
## 6     0.000000e+00  1.1385057 16.666667     -0.30570207     -0.67964897
##   peg.no2.rawstd peg.pm25.centstd peg.pm10.centstd peg.no2.centstd
## 1     0.75000405       1.01022672       0.50563793      0.75000405
## 2     1.10560349       0.61702476      -0.06745438      1.10560349
## 3     0.24290440      -0.05278783      -0.27264880      0.24290440
## 4     0.04584674       0.45031912       0.35250088      0.04584674
## 5     1.23726055       0.19928082      -0.43682360      1.23726055
## 6     2.21626254      -0.30570207      -0.67964897      2.21626254
##   peg.pm25.centscalestd peg.pm10.centscalestd peg.no2.centscalestd
## 1             0.6250130           0.095720013            0.5490042
## 2             0.3566326          -0.067464438            0.8698305
## 3            -0.2473542          -0.415051458            0.2669031
## 4             0.1430639          -0.006709575            0.1354871
## 5             0.2366308          -0.160160480            1.1554903
## 6            -0.3437592          -0.266668345            1.8896087
##   birth_pseudtract birth.pm25 birth.pm10 age1_pseudtract age1.pm25 age1.pm10
## 1             2089   18.87445         NA            2089  22.04215 11.062638
## 2             2089   18.87445         NA            2089  22.04215 11.062638
## 3             4042   18.00709         NA            4042  20.35600  8.967897
## 4             4042   18.00709         NA            4042  20.35600  8.967897
## 5             4070   15.80071         NA            4070  19.82025  9.275654
## 6             4070   15.80071         NA            4070  19.82025  9.275654
##   age3_pseudtract age3.pm25 age3.pm10           methid0 childteen0 cm5b_age
## 1            2089  22.44949 13.849589 3999984114_R02C02          C      117
## 2            2089  22.44949 13.849589 3999984114_R02C02          C      117
## 3            4499  14.54962  7.848451 3999932091_R04C02          C      116
## 4            4499  14.54962  7.848451 3999932091_R04C02          C      116
## 5            2971  22.05678  9.214114 3999932091_R01C01          C      118
## 6            2971  22.05678  9.214114 3999932091_R01C01          C      118
##   cm5b_ageyrs cp6yagey cp6yagem ck6yagey ck6yagem qc_flag_any0 restoration0
## 1        9.65       16      194       16      194        FALSE        FALSE
## 2        9.65       16      194       16      194        FALSE        FALSE
## 3        9.55       16      194       16      194        FALSE        FALSE
## 4        9.55       16      194       16      194        FALSE        FALSE
## 5        9.73       17      200       17      200        FALSE        FALSE
## 6        9.73       17      200       17      200        FALSE        FALSE
##   staining_green0 staining_red0 extension_green0 extension_red0
## 1           FALSE         FALSE            FALSE          FALSE
## 2           FALSE         FALSE            FALSE          FALSE
## 3           FALSE         FALSE            FALSE          FALSE
## 4           FALSE         FALSE            FALSE          FALSE
## 5           FALSE         FALSE            FALSE          FALSE
## 6           FALSE         FALSE            FALSE          FALSE
##   hybridization_highmedium0 hybridization_mediumlow0 target_removal_10
## 1                     FALSE                    FALSE             FALSE
## 2                     FALSE                    FALSE             FALSE
## 3                     FALSE                    FALSE             FALSE
## 4                     FALSE                    FALSE             FALSE
## 5                     FALSE                    FALSE             FALSE
## 6                     FALSE                    FALSE             FALSE
##   target_removal_20 bisulfite_conversion_i_green0 bisulfite_conversion_i_red0
## 1             FALSE                         FALSE                       FALSE
## 2             FALSE                         FALSE                       FALSE
## 3             FALSE                         FALSE                       FALSE
## 4             FALSE                         FALSE                       FALSE
## 5             FALSE                         FALSE                       FALSE
## 6             FALSE                         FALSE                       FALSE
##   bisulfite_conversion_ii0 specificity_i_green0 specificity_i_red0
## 1                    FALSE                FALSE              FALSE
## 2                    FALSE                FALSE              FALSE
## 3                    FALSE                FALSE              FALSE
## 4                    FALSE                FALSE              FALSE
## 5                    FALSE                FALSE              FALSE
## 6                    FALSE                FALSE              FALSE
##   specificity_ii0 nonpolymorphic_green0 nonpolymorphic_red0        x0
## 1           FALSE                 FALSE               FALSE 1.0828574
## 2           FALSE                 FALSE               FALSE 1.0828574
## 3           FALSE                 FALSE               FALSE 1.0700698
## 4           FALSE                 FALSE               FALSE 1.0700698
## 5           FALSE                 FALSE               FALSE 0.7313603
## 6           FALSE                 FALSE               FALSE 0.7313603
##           y0 sex0 predicted_sex0 predicted_sex_outlier0 probe_fail_pct0
## 1 0.10200305    f              f                  FALSE      0.02217723
## 2 0.10200305    f              f                  FALSE      0.02217723
## 3 0.09623082    f              f                  FALSE      0.03764659
## 4 0.09623082    f              f                  FALSE      0.03764659
## 5 0.83475864    m              m                  FALSE      0.02167976
## 6 0.83475864    m              m                  FALSE      0.02167976
##   probe_fail_100 probe_fail_050 snp_guess0 snp_logodd_outlier0 snp_unexpected0
## 1          FALSE          FALSE        638           -5.063077           FALSE
## 2          FALSE          FALSE        638           -5.063077           FALSE
## 3          FALSE          FALSE        552           -4.888753           FALSE
## 4          FALSE          FALSE        552           -4.888753           FALSE
## 5          FALSE          FALSE        545           -5.292545           FALSE
## 6          FALSE          FALSE        545           -5.292545           FALSE
##       mqc0     uqc0 low_int0 bad_sample0 horvath_clock_20130
## 1 11.78218 12.27845    FALSE           0            9.822213
## 2 11.78218 12.27845    FALSE           0            9.822213
## 3 11.47725 11.93590    FALSE           0            7.441843
## 4 11.47725 11.93590    FALSE           0            7.441843
## 5 11.66400 12.24615    FALSE           0           14.019331
## 6 11.66400 12.24615    FALSE           0           14.019331
##   horvath_clock_20180 levine_clock_20180 pediatric0 bmi_hamilton0
## 1            6.946498           4.472997   8.770975      4.558525
## 2            6.946498           4.472997   8.770975      4.558525
## 3            7.402976          10.815426   8.716650      8.378462
## 4            7.402976          10.815426   8.716650      8.378462
## 5           10.083214           2.967176   9.721045      4.438685
## 6           10.083214           2.967176   9.721045      4.438685
##   bmi_mccartney0 bmi_wahl0    smoke0       tnfa0   particles0 momsmoke0
## 1     -0.7955942  491.5261 -13.20036  0.01191998 -0.003662677  2.634444
## 2     -0.7955942  491.5261 -13.20036  0.01191998 -0.003662677  2.634444
## 3     -0.6219595  496.2639 -12.91928  0.01306847 -0.004819069  2.612985
## 4     -0.6219595  496.2639 -12.91928  0.01306847 -0.004819069  2.612985
## 5     -0.6217506  502.1522 -14.28656 -0.01103089 -0.003486200  2.612230
## 6     -0.6217506  502.1522 -14.28656 -0.01103089 -0.003486200  2.612230
##       mombmi0 inflammation0           methid1 childteen1 qc_flag_any1
## 1 -0.02699954    0.04969880 3999984114_R01C02          T        FALSE
## 2 -0.02699954    0.04969880 3999984114_R01C02          T        FALSE
## 3 -0.02932119    0.04437617 3999932092_R04C02          T        FALSE
## 4 -0.02932119    0.04437617 3999932092_R04C02          T        FALSE
## 5 -0.02986765    0.03602437 3999932092_R01C01          T        FALSE
## 6 -0.02986765    0.03602437 3999932092_R01C01          T        FALSE
##   restoration1 staining_green1 staining_red1 extension_green1 extension_red1
## 1        FALSE           FALSE            NA            FALSE          FALSE
## 2        FALSE           FALSE            NA            FALSE          FALSE
## 3        FALSE           FALSE         FALSE            FALSE          FALSE
## 4        FALSE           FALSE         FALSE            FALSE          FALSE
## 5        FALSE           FALSE         FALSE            FALSE          FALSE
## 6        FALSE           FALSE         FALSE            FALSE          FALSE
##   hybridization_highmedium1 hybridization_mediumlow1 target_removal_11
## 1                     FALSE                    FALSE             FALSE
## 2                     FALSE                    FALSE             FALSE
## 3                     FALSE                    FALSE             FALSE
## 4                     FALSE                    FALSE             FALSE
## 5                     FALSE                    FALSE             FALSE
## 6                     FALSE                    FALSE             FALSE
##   target_removal_21 bisulfite_conversion_i_green1 bisulfite_conversion_i_red1
## 1             FALSE                         FALSE                       FALSE
## 2             FALSE                         FALSE                       FALSE
## 3             FALSE                         FALSE                       FALSE
## 4             FALSE                         FALSE                       FALSE
## 5             FALSE                         FALSE                       FALSE
## 6             FALSE                         FALSE                       FALSE
##   bisulfite_conversion_ii1 specificity_i_green1 specificity_i_red1
## 1                    FALSE                FALSE              FALSE
## 2                    FALSE                FALSE              FALSE
## 3                    FALSE                FALSE              FALSE
## 4                    FALSE                FALSE              FALSE
## 5                    FALSE                FALSE              FALSE
## 6                    FALSE                FALSE              FALSE
##   specificity_ii1 nonpolymorphic_green1 nonpolymorphic_red1        x1
## 1           FALSE                 FALSE               FALSE 1.1083698
## 2           FALSE                 FALSE               FALSE 1.1083698
## 3           FALSE                 FALSE               FALSE 1.0874372
## 4           FALSE                 FALSE               FALSE 1.0874372
## 5           FALSE                 FALSE               FALSE 0.7097321
## 6           FALSE                 FALSE               FALSE 0.7097321
##           y1 sex1 predicted_sex1 predicted_sex_outlier1 probe_fail_pct1
## 1 0.09767087    f              f                  FALSE     0.005142331
## 2 0.09767087    f              f                  FALSE     0.005142331
## 3 0.08934078    f              f                  FALSE     0.014520544
## 4 0.08934078    f              f                  FALSE     0.014520544
## 5 0.83383358    m              m                  FALSE     0.024064099
## 6 0.83383358    m              m                  FALSE     0.024064099
##   probe_fail_101 probe_fail_051 snp_guess1 snp_logodd_outlier1 snp_unexpected1
## 1          FALSE          FALSE        638           -5.308726           FALSE
## 2          FALSE          FALSE        638           -5.308726           FALSE
## 3          FALSE          FALSE        552           -5.063976           FALSE
## 4          FALSE          FALSE        552           -5.063976           FALSE
## 5          FALSE          FALSE        545           -4.972307           FALSE
## 6          FALSE          FALSE        545           -4.972307           FALSE
##       mqc1     uqc1 low_int1 bad_sample1 horvath_clock_20131
## 1 12.01472 12.56914    FALSE           0            16.06603
## 2 12.01472 12.56914    FALSE           0            16.06603
## 3 12.06002 12.54037    FALSE           0            12.81881
## 4 12.06002 12.54037    FALSE           0            12.81881
## 5 11.63708 12.14689    FALSE           0            20.83949
## 6 11.63708 12.14689    FALSE           0            20.83949
##   horvath_clock_20181 levine_clock_20181 pediatric1 idnum21 list1 bmi_hamilton1
## 1            14.47595           17.53386   12.43236 1000002  1500      7.330503
## 2            14.47595           17.53386   12.43236 1000002  1500      7.330503
## 3            13.29797           18.26464   12.37133 1000005  1341      8.750580
## 4            13.29797           18.26464   12.37133 1000005  1341      8.750580
## 5            16.49349           17.00538   10.89212 1000019  1335      3.757208
## 6            16.49349           17.00538   10.89212 1000019  1335      3.757208
##   bmi_mccartney1 bmi_wahl1    smoke1       tnfa1   particles1 momsmoke1
## 1     -0.7542774  473.2941 -10.44782  0.01448388 -0.004139335  2.726711
## 2     -0.7542774  473.2941 -10.44782  0.01448388 -0.004139335  2.726711
## 3     -0.7252045  484.0722 -11.07321  0.01258851 -0.005083212  2.606195
## 4     -0.7252045  484.0722 -11.07321  0.01258851 -0.005083212  2.606195
## 5     -0.5783662  537.1475 -15.36230 -0.01093033 -0.002173911  2.603815
## 6     -0.5783662  537.1475 -15.36230 -0.01093033 -0.002173911  2.603815
##       mombmi1 inflammation1 potbad race childsex female cm1relf cm2povco
## 1 -0.02566669    0.07408101      0    2        2      1       3      0.6
## 2 -0.02566669    0.07408101      0    2        2      1       3      0.6
## 3 -0.02728736    0.06852657      0    2        2      1       2      0.1
## 4 -0.02728736    0.06852657      0    2        2      1       2      0.1
## 5 -0.03069736    0.03647710      0    2        1      0       4      0.3
## 6 -0.03069736    0.03647710      0    2        1      0       4      0.3
##   cm3povco cm4povco  cm5povco   cp6povco cf1ethrace ewastoolsleukocytes0
## 1      0.5      0.2 0.0000000 0.16062708          2             0.990808
## 2      0.5      0.2 0.0000000 0.16062708          2             0.990808
## 3      0.1      0.1 0.4318411 0.03105924          2             1.038586
## 4      0.1      0.1 0.4318411 0.03105924          2             1.038586
## 5      0.3      0.3 0.1799338 0.18683441          2             1.090425
## 6      0.3      0.3 0.1799338 0.18683441          2             1.090425
##   ewastoolsepithelialcells0 epidishrpcepi0 epidishrpcfib0 epidishrpcic0
## 1             -1.387779e-17      0.2543087     0.02423720     0.7214541
## 2             -1.387779e-17      0.2543087     0.02423720     0.7214541
## 3              0.000000e+00      0.2990498     0.02308954     0.6778606
## 4              0.000000e+00      0.2990498     0.02308954     0.6778606
## 5              0.000000e+00      0.1458830     0.02784315     0.8262739
## 6              0.000000e+00      0.1458830     0.02784315     0.8262739
##   epidishcbsepi0 epidishcbsfib0 epidishcbsic0 epidishcpepi0 epidishcpfib0
## 1      0.2496210     0.01722156     0.7331575    0.17778499    0.07339475
## 2      0.2496210     0.01722156     0.7331575    0.17778499    0.07339475
## 3      0.2862059     0.02414045     0.6896537    0.22135556    0.10390326
## 4      0.2862059     0.02414045     0.6896537    0.22135556    0.10390326
## 5      0.1404656     0.02146553     0.8380688    0.07960265    0.06765931
## 6      0.1404656     0.02146553     0.8380688    0.07960265    0.06765931
##   epidishcpic0 ewastoolsleukocytes1 ewastoolsepithelialcells1 epidishrpcepi1
## 1    0.6284974            0.7876077                 0.1721681      0.4684542
## 2    0.6284974            0.7876077                 0.1721681      0.4684542
## 3    0.5998775            0.8137317                 0.1804163      0.4434395
## 4    0.5998775            0.8137317                 0.1804163      0.4434395
## 5    0.7217021            1.1385057                 0.0000000      0.1112593
## 6    0.7217021            1.1385057                 0.0000000      0.1112593
##   epidishrpcfib1 epidishrpcic1 epidishcbsepi1 epidishcbsfib1 epidishcbsic1
## 1     0.00000000     0.5315458      0.4533455     0.00000000     0.5466545
## 2     0.00000000     0.5315458      0.4533455     0.00000000     0.5466545
## 3     0.00000000     0.5565605      0.4309103     0.00000000     0.5690897
## 4     0.00000000     0.5565605      0.4309103     0.00000000     0.5690897
## 5     0.02012005     0.8686206      0.1084706     0.01871887     0.8728105
## 6     0.02012005     0.8686206      0.1084706     0.01871887     0.8728105
##   epidishcpepi1 epidishcpfib1 epidishcpic1 birth.pm10.na birth.pm25.na
## 1    0.34523654    0.07368201    0.4673083       Missing          <NA>
## 2    0.34523654    0.07368201    0.4673083       Missing          <NA>
## 3    0.32897812    0.08255456    0.4914306       Missing          <NA>
## 4    0.32897812    0.08255456    0.4914306       Missing          <NA>
## 5    0.04829928    0.06247644    0.7589061       Missing          <NA>
## 6    0.04829928    0.06247644    0.7589061       Missing          <NA>
##   age1.pm10.na age1.pm25.na age3.pm10.na age3.pm25.na childethrace.factor
## 1         <NA>         <NA>         <NA>         <NA>  Non-Hispanic Black
## 2         <NA>         <NA>         <NA>         <NA>  Non-Hispanic Black
## 3         <NA>         <NA>         <NA>         <NA>  Non-Hispanic Black
## 4         <NA>         <NA>         <NA>         <NA>  Non-Hispanic Black
## 5         <NA>         <NA>         <NA>         <NA>  Non-Hispanic Black
## 6         <NA>         <NA>         <NA>         <NA>  Non-Hispanic Black
##       matrace.factor m1b2.na m1b2.factor m1city.factor epithelial.percent
## 1 Non-Hispanic Black    <NA> Not Married       Oakland       1.880264e+01
## 2 Non-Hispanic Black    <NA> Not Married       Oakland      -1.387779e-15
## 3 Non-Hispanic Black    <NA> Not Married       Oakland       0.000000e+00
## 4 Non-Hispanic Black    <NA> Not Married       Oakland       1.827235e+01
## 5 Non-Hispanic Black    <NA> Not Married       Oakland       0.000000e+00
## 6 Non-Hispanic Black    <NA> Not Married       Oakland       0.000000e+00
##   immune.percent cg00905156.percent cg06849931.percent cg15082635.percent
## 1       81.19736           2.103662           58.20260           2.077820
## 2      100.00000           2.367230           73.76557           1.468084
## 3      100.00000           1.343715           70.65934           1.371734
## 4       81.72765           2.153345           60.33457           1.652054
## 5      100.00000           1.835185           84.37224           1.634140
## 6      100.00000           1.950365           89.74772           1.465849
##   cg18640183.percent cg20340716.percent cg24127244.percent sex.na childteen.na
## 1           5.230433           92.82739           2.252754   <NA>         <NA>
## 2           5.154276           92.14317           2.162791   <NA>         <NA>
## 3           3.810300           94.76437           1.497054   <NA>         <NA>
## 4           4.436995           93.31084           2.591302   <NA>         <NA>
## 5           5.221866           91.36594           2.034587   <NA>         <NA>
## 6           4.878337           91.57463           1.992653   <NA>         <NA>
##   age.na m1city.na ethrace.factor.na race.factor.na cm1inpov.na peg.pm25.raw.na
## 1   <NA>      <NA>              <NA>           <NA>        <NA>            <NA>
## 2   <NA>      <NA>              <NA>           <NA>        <NA>            <NA>
## 3   <NA>      <NA>              <NA>           <NA>        <NA>            <NA>
## 4   <NA>      <NA>              <NA>           <NA>        <NA>            <NA>
## 5   <NA>      <NA>              <NA>           <NA>        <NA>            <NA>
## 6   <NA>      <NA>              <NA>           <NA>        <NA>            <NA>
##   peg.pm10.raw.na peg.no2.raw.na immune.na epithelial.na include
## 1            <NA>           <NA>      <NA>          <NA>       1
## 2            <NA>           <NA>      <NA>          <NA>       1
## 3            <NA>           <NA>      <NA>          <NA>       1
## 4            <NA>           <NA>      <NA>          <NA>       1
## 5            <NA>           <NA>      <NA>          <NA>       1
## 6            <NA>           <NA>      <NA>          <NA>       1
output<-data.frame(matrix(nrow=10, ncol= 16))
colnames(output) <- c("Exposure", "Age", "Model", "DF", "IQRcoef.r", "IQRlcl.r", "IQRucl.r", "pval.r",  "IQRcoef.c", "IQRlcl.c", "IQRucl.c", "pval.c",  "IQRcoef.cs", "IQRlcl.cs", "IQRucl.cs", "pval.cs")
output$Exposure <- c(rep(c("PM2.5", "PM10"), 5))
output$Age <- c(rep("All",10 ))
output$Model <- c(rep("Primary", 2), rep("Age1", 2), rep("Age3", 2), rep("Coexposure", 2), rep("NOx", 2))

IQR(include$birth.pm25, na.rm=T)
## [1] 10.74194
IQR(include$birth.pm10, na.rm=T)
## [1] 3.223546
# Primary model
mod.pm25<-lme(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, random = ~ 1 | idnum, data=include, na.action=na.exclude)
summary(mod)$tTable[2,1] # Beta coefficient "Value"
## NULL
summary(mod)$tTable[2,2] # Standard error
## NULL
summary(mod)$tTable[2,3] # DF
## NULL
summary(mod)$tTable[2,5] # p-value
## NULL
mod.pm10<-lme(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$DF[1:2] <-c(summary(mod.pm25)$tTable[2,3], summary(mod.pm10)$tTable[2,3])

# str(intervals(mod))
# intervals(mod)$fixed[2,1] #lower
# intervals(mod)$fixed[2,3] #upper

output$IQRcoef.r[1:2] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.r[1:2] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.r[1:2] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.r[1:2] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

# Age 1 sensitivity
mod.pm25<-lme(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm25, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm10, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$DF[3:4] <-c(summary(mod.pm25)$tTable[2,3], summary(mod.pm10)$tTable[2,3])

output$IQRcoef.r[3:4] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.r[3:4] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.r[3:4] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.r[3:4] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

# Age 3 sensitivity
mod.pm25<-lme(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm25, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm10, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$DF[5:6] <-c(summary(mod.pm25)$tTable[2,3], summary(mod.pm10)$tTable[2,3])

output$IQRcoef.r[5:6] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.r[5:6] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.r[5:6] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.r[5:6] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

# Co-exposure sensitivity

mod.pm25<-lme(peg.pm25.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm10, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.pm10.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm25, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$DF[7:8] <-c(summary(mod.pm25)$tTable[2,3], summary(mod.pm10)$tTable[2,3])

output$IQRcoef.r[7:8] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.r[7:8] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.r[7:8] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.r[7:8] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

# NOX sensitivity

mod.pm25<-lme(peg.no2.rawstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.no2.rawstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$DF[9:10] <-c(summary(mod.pm25)$tTable[2,3], summary(mod.pm10)$tTable[2,3])

output$IQRcoef.r[9:10] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.r[9:10] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.r[9:10] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.r[9:10] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

############################
#### Center peg.pm25.centstd

# Primary model
mod.pm25<-lme(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$IQRcoef.c[1:2] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.c[1:2] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.c[1:2] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.c[1:2] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

# Age 1 sensitivity
mod.pm25<-lme(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm25, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm10, random = ~ 1 | idnum, data=include, na.action=na.exclude)


output$IQRcoef.c[3:4] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.c[3:4] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.c[3:4] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.c[3:4] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

# Age 3 sensitivity
mod.pm25<-lme(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm25, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm10, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$IQRcoef.c[5:6] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.c[5:6] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.c[5:6] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.c[5:6] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

# Co-exposure sensitivity

mod.pm25<-lme(peg.pm25.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm10, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.pm10.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm25, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$IQRcoef.c[7:8] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.c[7:8] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.c[7:8] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.c[7:8] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

# NOX sensitivity

mod.pm25<-lme(peg.no2.centstd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.no2.centstd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$IQRcoef.c[9:10] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.c[9:10] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.c[9:10] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.c[9:10] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

#### Center Scaled peg.pm10.centscalestd 

# Primary model
mod.pm25<-lme(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$IQRcoef.cs[1:2] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.cs[1:2] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.cs[1:2] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.cs[1:2] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

# Age 1 sensitivity
mod.pm25<-lme(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm25, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age1.pm10, random = ~ 1 | idnum, data=include, na.action=na.exclude)


output$IQRcoef.cs[3:4] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.cs[3:4] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.cs[3:4] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.cs[3:4] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

# Age 3 sensitivity
mod.pm25<-lme(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm25, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + age3.pm10, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$IQRcoef.cs[5:6] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.cs[5:6] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.cs[5:6] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.cs[5:6] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

# Co-exposure sensitivity

mod.pm25<-lme(peg.pm25.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm10, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.pm10.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes + birth.pm25, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$IQRcoef.cs[7:8] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.cs[7:8] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.cs[7:8] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.cs[7:8] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])

# NOX sensitivity

mod.pm25<-lme(peg.no2.centscalestd ~ birth.pm25 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, random = ~ 1 | idnum, data=include, na.action=na.exclude)

mod.pm10<-lme(peg.no2.centscalestd ~ birth.pm10 + age.dnam + sex + matrace.factor + cm1inpov + m1b2 + Epithelial.cells + Leukocytes, random = ~ 1 | idnum, data=include, na.action=na.exclude)

output$IQRcoef.cs[9:10] <- c(IQR(include$birth.pm25, na.rm=T) * c(summary(mod.pm25)$tTable[2,1], IQR(include$birth.pm10, na.rm=T) * summary(mod.pm10)$tTable[2,1]))

output$IQRlcl.cs[9:10] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,1], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,1]))

output$IQRucl.cs[9:10] <- c(IQR(include$birth.pm25, na.rm=T) * c(intervals(mod.pm25)$fixed[2,3], IQR(include$birth.pm10, na.rm=T) * intervals(mod.pm10)$fixed[2,3]))

output$pval.cs[9:10] <-c(summary(mod.pm25)$tTable[2,5], summary(mod.pm10)$tTable[2,5])



output
##    Exposure Age      Model  DF   IQRcoef.r    IQRlcl.r     IQRucl.r
## 1     PM2.5 All    Primary 787 -0.02873504 -0.07324774  0.015777659
## 2      PM10 All    Primary 728 -0.14716531 -0.30380034  0.009469716
## 3     PM2.5 All       Age1 738 -0.01733323 -0.07063770  0.035971247
## 4      PM10 All       Age1 682 -0.24216639 -0.45457048 -0.029762301
## 5     PM2.5 All       Age3 714 -0.03162336 -0.08930200  0.026055274
## 6      PM10 All       Age3 666 -0.20037031 -0.42035751  0.019616883
## 7     PM2.5 All Coexposure 727 -0.02783807 -0.08061208  0.024935936
## 8      PM10 All Coexposure 727 -0.14739919 -0.30788733  0.013088943
## 9     PM2.5 All        NOx 787  0.02919143 -0.05510955  0.113492404
## 10     PM10 All        NOx 728  1.41661537  0.70185524  2.131375492
##          pval.r   IQRcoef.c    IQRlcl.c     IQRucl.c       pval.c   IQRcoef.cs
## 1  0.2054594307 -0.02873504 -0.07324774  0.015777659 0.2054594307 -0.016707301
## 2  0.0655110474 -0.14716531 -0.30380034  0.009469716 0.0655110473 -0.132834150
## 3  0.5234268630 -0.01733323 -0.07063770  0.035971247 0.5234268630 -0.005489542
## 4  0.0255057953 -0.24216639 -0.45457048 -0.029762301 0.0255057952 -0.208649113
## 5  0.2821072200 -0.03162336 -0.08930200  0.026055274 0.2821072200 -0.018901365
## 6  0.0741597166 -0.20037031 -0.42035751  0.019616883 0.0741597162 -0.169874718
## 7  0.3007345192 -0.02783807 -0.08061208  0.024935936 0.3007345192 -0.011909817
## 8  0.0717838008 -0.14739919 -0.30788733  0.013088943 0.0717838007 -0.145344782
## 9  0.4968725601  0.02919143 -0.05510955  0.113492401 0.4968725377  0.007053005
## 10 0.0001089792  1.41661537  0.70185524  2.131375490 0.0001089792  1.066075193
##      IQRlcl.cs     IQRucl.cs      pval.cs
## 1  -0.05139483  0.0179802258 0.3447072533
## 2  -0.27387976  0.0082114596 0.0648729087
## 3  -0.04706967  0.0360905876 0.7955641642
## 4  -0.39966480 -0.0176334255 0.0323293250
## 5  -0.06375523  0.0259524992 0.4083270661
## 6  -0.36765293  0.0279034925 0.0921663503
## 7  -0.05294905  0.0291294137 0.5690292787
## 8  -0.28979073 -0.0008988394 0.0485950295
## 9  -0.06076665  0.0748726595 0.8382943029
## 10  0.48776642  1.6443839606 0.0003161086
write.csv(output, file=here("Output",paste0("FFCW_AirPoll_MixedEffects_Regression_Primary_", date, ".csv")) )